You can find a searchable list of my publications below. My Google Scholar profile contains an up-to-date overview of my citations. I also have a ResearchGate profile with most of my full-texts.
I am a strong proponent of Open Access, especially after having spent more than four years as a researcher at an institution with a very limited number of journal subscriptions. For each entry below where I am legally allowed to share a full-text, you can find it as the first link of the entry.
2025
Viggen, Erlend Magnus (Ed.)
Proceedings of the 48th Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2025, ISBN: 978-82-8123-025-5.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2025,
title = {Proceedings of the 48th Scandinavian Symposium on Physical Acoustics},
editor = {Viggen, Erlend Magnus},
url = {https://www.researchgate.net/publication/391666191_Proceedings_of_the_48th_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-025-5},
year = {2025},
date = {2025-05-01},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Diez, Anja; Viggen, Erlend Magnus; Johansen, Tonni Franke
Ultrasonic pulse-echo dataset from numerical modelling for oil and gas well integrity investigations Journal Article
In: Scientific Data, vol. 12, pp. 7, 2025, ISSN: 2052-4463.
Abstract | BibTeX | Tags: acoustics, well logging | Links:
@article{diez_2025_ultrasonic,
title = {Ultrasonic pulse-echo dataset from numerical modelling for oil and gas well integrity investigations},
author = {Diez, Anja and Viggen, Erlend Magnus and Johansen, Tonni Franke},
doi = {10.1038/s41597-025-04851-x},
issn = {2052-4463},
year = {2025},
date = {2025-04-01},
journal = {Scientific Data},
volume = {12},
pages = {7},
abstract = {The ultrasonic pulse-echo (PE) measurement is a crucial measurement technique to determine the integrity of oil and gas wells. Oil companies use various analysis techniques and corrections to derive the pipe thickness and impedance of the material behind the pipe from PE measurements that are carried out inside the pipe. While some field measurements are publicly available, they have no corresponding ground truth. We therefore simulated a dataset of PE measurements with ground truth. The dataset was generated using axisymmetric models and 3D models in COMSOL Multiphysics. The base geometry was based on common parameters from the field: oil-based mud on the inside of a 9.625 in pipe and cement on the outside of the pipe. From this base geometry, variations in the model parameters were introduced, for example, plate/pipe wall thickness, different materials on both sides of the wall, different pipe diameter, different annulus thicknesses, eccentering. The generated dataset allows detailed investigations of existing PE analysis algorithms, comparison of those and development of new PE analysis techniques.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {article}
}
The ultrasonic pulse-echo (PE) measurement is a crucial measurement technique to determine the integrity of oil and gas wells. Oil companies use various analysis techniques and corrections to derive the pipe thickness and impedance of the material behind the pipe from PE measurements that are carried out inside the pipe. While some field measurements are publicly available, they have no corresponding ground truth. We therefore simulated a dataset of PE measurements with ground truth. The dataset was generated using axisymmetric models and 3D models in COMSOL Multiphysics. The base geometry was based on common parameters from the field: oil-based mud on the inside of a 9.625 in pipe and cement on the outside of the pipe. From this base geometry, variations in the model parameters were introduced, for example, plate/pipe wall thickness, different materials on both sides of the wall, different pipe diameter, different annulus thicknesses, eccentering. The generated dataset allows detailed investigations of existing PE analysis algorithms, comparison of those and development of new PE analysis techniques.
Viggen, Erlend Magnus; Grønsberg, Sondre; Brekke, Svein; Hicks, Brad; Wifstad, Sigurd Vangen
Improving pipe perforation estimates from ultrasonic imaging using subpixel machine learning trained on optical data Journal Article
In: Geoenergy Science and Engineering, vol. 246, pp. 9, 2025, ISSN: 2949-8910.
Abstract | BibTeX | Tags: acoustics, machine learning, well logging | Links:
@article{viggen_2025_improving,
title = {Improving pipe perforation estimates from ultrasonic imaging using subpixel machine learning trained on optical data},
author = {Viggen, Erlend Magnus and Grønsberg, Sondre and Brekke, Svein and Hicks, Brad and Wifstad, Sigurd Vangen},
doi = {10.1016/j.geoen.2024.213541},
issn = {2949-8910},
year = {2025},
date = {2025-03-01},
journal = {Geoenergy Science and Engineering},
volume = {246},
pages = {9},
abstract = {During well completion, well pipes are perforated to gain access the reservoir. The size and shape of the perforated holes can be found from these holes’ outlines, which are generally estimated by optical or ultrasonic image logging. While optical imaging typically has a much higher spatial resolution than ultrasonic imaging and thus allows more precise outline estimates, optical imaging requires transparent liquids inside the pipe. Ultrasonic imaging, on the other hand, can be performed in a wider variety of liquids and can provide further information about the well state from the same measurement. One strategy is therefore to combine both types of measurement on the same toolstring. Thus, ultrasonic imaging can take over the job of estimating hole outlines from optical imaging when the liquid is no longer sufficiently transparent. One issue with this strategy is that the agreement between these two imaging techniques currently leaves much to be desired. This work addresses this issue by training a machine learning (ML)-based subpixel segmentation algorithm to take ultrasonic images of perforations and reproduce perforation outline estimates made from optical images. This approach assists the algorithm in drawing out information from the ultrasonic data which is not easily accessible using traditional image processing techniques. We use a dataset of 390 perforations, measured by both an optical and an ultrasonic tool, to train and test the machine learning algorithm. For comparison, we use a baseline algorithm based on interpolation and image thresholding. We evaluate the algorithms’ performance according to their estimated outlines’ match with the optical outlines. The outlines’ overlap is quantified via the intersection over union metric (baseline: 50.8%, ML: 74.2%; higher is better), and their area match is quantified via mean relative area error and compared to the results of another study from the literature (baseline: 54.4%, ML: 18.7%, other study: 54.5%; lower is better).},
keywords = {acoustics, machine learning, well logging},
pubstate = {published},
tppubtype = {article}
}
During well completion, well pipes are perforated to gain access the reservoir. The size and shape of the perforated holes can be found from these holes’ outlines, which are generally estimated by optical or ultrasonic image logging. While optical imaging typically has a much higher spatial resolution than ultrasonic imaging and thus allows more precise outline estimates, optical imaging requires transparent liquids inside the pipe. Ultrasonic imaging, on the other hand, can be performed in a wider variety of liquids and can provide further information about the well state from the same measurement. One strategy is therefore to combine both types of measurement on the same toolstring. Thus, ultrasonic imaging can take over the job of estimating hole outlines from optical imaging when the liquid is no longer sufficiently transparent. One issue with this strategy is that the agreement between these two imaging techniques currently leaves much to be desired. This work addresses this issue by training a machine learning (ML)-based subpixel segmentation algorithm to take ultrasonic images of perforations and reproduce perforation outline estimates made from optical images. This approach assists the algorithm in drawing out information from the ultrasonic data which is not easily accessible using traditional image processing techniques. We use a dataset of 390 perforations, measured by both an optical and an ultrasonic tool, to train and test the machine learning algorithm. For comparison, we use a baseline algorithm based on interpolation and image thresholding. We evaluate the algorithms’ performance according to their estimated outlines’ match with the optical outlines. The outlines’ overlap is quantified via the intersection over union metric (baseline: 50.8%, ML: 74.2%; higher is better), and their area match is quantified via mean relative area error and compared to the results of another study from the literature (baseline: 54.4%, ML: 18.7%, other study: 54.5%; lower is better).
Roberts, Glyn; Saha, Souvick; Moreno, Shane; Viggen, Erlend Magnus; Tymons, Tobben
AI Enhanced Multi-Physics Imaging for Perforation Erosion Analysis: A Quantum Shift in the Accuracy and Efficiency of Unconventional Well Optimization Proceedings Article
In: SPE Hydraulic Fracturing Technology Conference and Exhibition, pp. 17, Society of Petroleum Engineers, 2025, ISBN: 978-1-959025-57-3 .
Abstract | BibTeX | Tags: well logging | Links:
@inproceedings{nokey,
title = {AI Enhanced Multi-Physics Imaging for Perforation Erosion Analysis: A Quantum Shift in the Accuracy and Efficiency of Unconventional Well Optimization},
author = {Glyn Roberts and Souvick Saha and Shane Moreno and Erlend Magnus Viggen and Tobben Tymons},
doi = {10.2118/223570-MS},
isbn = {978-1-959025-57-3 },
year = {2025},
date = {2025-02-04},
urldate = {2025-02-04},
booktitle = {SPE Hydraulic Fracturing Technology Conference and Exhibition},
pages = {17},
publisher = {Society of Petroleum Engineers},
abstract = {This paper demonstrates how the integration of multi-physics downhole imaging with machine learning techniques provides a step-change in perforation erosion analysis. We present a novel approach that improves measurement accuracy, consistency, and turnaround time. We reveal how this benefits the fields of completion design and optimisation of hydraulic fracturing, and how this enables gains in productivity and reduction in cost in the development of unconventional wells.
Downhole data is acquired from an integrated array video and phased array ultrasound sensor system. The video is analysed by a two-stage artificial intelligence process to identify perforations within the well and measure their geometries. High quality optical image results are used to train a third artificial intelligence algorithm that identifies and analyses the same perforation geometries from the corresponding ultrasound dataset.
This approach reveals trends in erosional metal loss induced by proppant flow through perforations. This quantitative evaluation of proppant placement and analysis of its uniformity enables engineers to compare the effectiveness of completion designs and the frac operations.
Through experimental data, real-world case studies, and comparisons with conventional workflows, we demonstrate:
1. Acceleration of the analysis process through automation
2. Improved data quality and consistency by minimizing human error and subjectivity
3. How the use of the video data, with a resolution ten times greater than the ultrasound sensor, as a ground truth and training parameter has been used to dramatically improve the correlation of the two datasets, creating a highly reliable determination of perforation geometries in the widest possible range of well conditions.
Specific case studies included within the paper are as follows:
1. The application of downhole video measurements, and the evolution of machine learning-enhanced processing. We chart the speed, accuracy and consistency of results from conventional and machine learning-enhanced processes.
2. The integration of video and ultrasound measurements, the correlation between datasets, and the improvements in data integrity resulting from a multi-physics approach.
3. The application of artificial intelligence to optimise measurements from the ultrasound sensor trained using video measurements, and the improvements in the accuracy and reliability of the resulting dataset.
Analysis techniques have been refined and training algorithms developed using both controlled surface tests and a database of over 100,000 perforation images obtained from hydraulically fractured wells. This paper presents the first publication of this unique three-tier artificial intelligence approach, and the results obtained from its application in the field.},
keywords = {well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper demonstrates how the integration of multi-physics downhole imaging with machine learning techniques provides a step-change in perforation erosion analysis. We present a novel approach that improves measurement accuracy, consistency, and turnaround time. We reveal how this benefits the fields of completion design and optimisation of hydraulic fracturing, and how this enables gains in productivity and reduction in cost in the development of unconventional wells.
Downhole data is acquired from an integrated array video and phased array ultrasound sensor system. The video is analysed by a two-stage artificial intelligence process to identify perforations within the well and measure their geometries. High quality optical image results are used to train a third artificial intelligence algorithm that identifies and analyses the same perforation geometries from the corresponding ultrasound dataset.
This approach reveals trends in erosional metal loss induced by proppant flow through perforations. This quantitative evaluation of proppant placement and analysis of its uniformity enables engineers to compare the effectiveness of completion designs and the frac operations.
Through experimental data, real-world case studies, and comparisons with conventional workflows, we demonstrate:
1. Acceleration of the analysis process through automation
2. Improved data quality and consistency by minimizing human error and subjectivity
3. How the use of the video data, with a resolution ten times greater than the ultrasound sensor, as a ground truth and training parameter has been used to dramatically improve the correlation of the two datasets, creating a highly reliable determination of perforation geometries in the widest possible range of well conditions.
Specific case studies included within the paper are as follows:
1. The application of downhole video measurements, and the evolution of machine learning-enhanced processing. We chart the speed, accuracy and consistency of results from conventional and machine learning-enhanced processes.
2. The integration of video and ultrasound measurements, the correlation between datasets, and the improvements in data integrity resulting from a multi-physics approach.
3. The application of artificial intelligence to optimise measurements from the ultrasound sensor trained using video measurements, and the improvements in the accuracy and reliability of the resulting dataset.
Analysis techniques have been refined and training algorithms developed using both controlled surface tests and a database of over 100,000 perforation images obtained from hydraulically fractured wells. This paper presents the first publication of this unique three-tier artificial intelligence approach, and the results obtained from its application in the field.
Downhole data is acquired from an integrated array video and phased array ultrasound sensor system. The video is analysed by a two-stage artificial intelligence process to identify perforations within the well and measure their geometries. High quality optical image results are used to train a third artificial intelligence algorithm that identifies and analyses the same perforation geometries from the corresponding ultrasound dataset.
This approach reveals trends in erosional metal loss induced by proppant flow through perforations. This quantitative evaluation of proppant placement and analysis of its uniformity enables engineers to compare the effectiveness of completion designs and the frac operations.
Through experimental data, real-world case studies, and comparisons with conventional workflows, we demonstrate:
1. Acceleration of the analysis process through automation
2. Improved data quality and consistency by minimizing human error and subjectivity
3. How the use of the video data, with a resolution ten times greater than the ultrasound sensor, as a ground truth and training parameter has been used to dramatically improve the correlation of the two datasets, creating a highly reliable determination of perforation geometries in the widest possible range of well conditions.
Specific case studies included within the paper are as follows:
1. The application of downhole video measurements, and the evolution of machine learning-enhanced processing. We chart the speed, accuracy and consistency of results from conventional and machine learning-enhanced processes.
2. The integration of video and ultrasound measurements, the correlation between datasets, and the improvements in data integrity resulting from a multi-physics approach.
3. The application of artificial intelligence to optimise measurements from the ultrasound sensor trained using video measurements, and the improvements in the accuracy and reliability of the resulting dataset.
Analysis techniques have been refined and training algorithms developed using both controlled surface tests and a database of over 100,000 perforation images obtained from hydraulically fractured wells. This paper presents the first publication of this unique three-tier artificial intelligence approach, and the results obtained from its application in the field.
2024
Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo
On the cutoff, reappearance, and persistence of subsonic leaky A0 Lamb waves Journal Article
In: Journal of Physics: Conference Series, vol. 2822, pp. 12, 2024, ISSN: 1742-6596.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@article{viggen_2024_cutoff,
title = {On the cutoff, reappearance, and persistence of subsonic leaky A0 Lamb waves},
author = {Viggen, Erlend Magnus and Arnestad, Håvard Kjellmo},
doi = {10.1088/1742-6596/2822/1/012168},
issn = {1742-6596},
year = {2024},
date = {2024-09-01},
journal = {Journal of Physics: Conference Series},
volume = {2822},
pages = {12},
abstract = {Subsonic leaky A0 Lamb waves, whose phase speeds are lower than the fluid's sound speed, exhibit several peculiar behaviours. In this work, we investigate three such behaviours: Cutoff, where the A0 branch cuts off at some point in the subsonic domain, reappearance, where the cut-off A0 branch reappears at lower frequencies, and persistence, where the A0 branch is never cut off at all. From the literature, we take the case of a 1 mm thick aluminium plate immersed in air of varying density. We then analyse its exact leaky A0 wave solutions by means of a sound radiation model that takes energy conservation into account. This analysis explains these peculiar behaviours by connecting them with the conditions that allow sound-radiating subsonic leaky A0 Lamb waves.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {article}
}
Subsonic leaky A0 Lamb waves, whose phase speeds are lower than the fluid's sound speed, exhibit several peculiar behaviours. In this work, we investigate three such behaviours: Cutoff, where the A0 branch cuts off at some point in the subsonic domain, reappearance, where the cut-off A0 branch reappears at lower frequencies, and persistence, where the A0 branch is never cut off at all. From the literature, we take the case of a 1 mm thick aluminium plate immersed in air of varying density. We then analyse its exact leaky A0 wave solutions by means of a sound radiation model that takes energy conservation into account. This analysis explains these peculiar behaviours by connecting them with the conditions that allow sound-radiating subsonic leaky A0 Lamb waves.
Wangensteen, Magnus; Johansen, Tonni Franke; Fatemi, Ali; Viggen, Erlend Magnus
Pipe wall thickness estimation by frequency–wavenumber analysis of circumferential guided waves Journal Article
In: Mechanical Systems and Signal Processing, vol. 215, pp. 111369, 2024.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@article{wangensteen_pipe_2024,
title = {Pipe wall thickness estimation by frequency–wavenumber analysis of circumferential guided waves},
author = {Magnus Wangensteen and Tonni Franke Johansen and Ali Fatemi and Erlend Magnus Viggen},
doi = {https://doi.org/10.1016/j.ymssp.2024.111369},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
journal = {Mechanical Systems and Signal Processing},
volume = {215},
pages = {111369},
abstract = {Ultrasonic guided waves in pipes propagating in the circumferential direction carry information about the thickness of the pipe wall. This study proposes a method for estimating the pipe wall thickness based on measurements from circumferentially distributed sensors and a set of pre-computed theoretical dispersion curves. The recorded data are Fourier transformed into a frequency–wavenumber representation. The wall thickness is obtained by identifying the best fitting dispersion curve to the frequency–wavenumber data by determining the maximum stacking power. The thickness estimation method has demonstrated robustness against noise. Given the presence of significant noise (0.0 dB signal-to-noise ratio) and simulations where the thickness ranges from 6.0 to 10.0 mm, using eight sensors or more attain a mean absolute percentage error of less than 1%. The method is also supported by real laboratory measurements using eight sensor positions, where the error is less than 0.7%. Experimental measurements near the pipe end demonstrate that with appropriate positioning, end-reflections should not have a detrimental effect on the accuracy. The method estimates the mean thickness in cases where the pipe wall has near-uniform thickness but may not be as reliable when there are large shallow defects caused by metal loss, which is typically the case when the inner wall has suffered from erosion. In this case, monitoring of the stacking power curves could detect regional metal loss due to erosion and analysis of the local maxima could uncover information about individual thickness segments around the pipe circumference.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {article}
}
Ultrasonic guided waves in pipes propagating in the circumferential direction carry information about the thickness of the pipe wall. This study proposes a method for estimating the pipe wall thickness based on measurements from circumferentially distributed sensors and a set of pre-computed theoretical dispersion curves. The recorded data are Fourier transformed into a frequency–wavenumber representation. The wall thickness is obtained by identifying the best fitting dispersion curve to the frequency–wavenumber data by determining the maximum stacking power. The thickness estimation method has demonstrated robustness against noise. Given the presence of significant noise (0.0 dB signal-to-noise ratio) and simulations where the thickness ranges from 6.0 to 10.0 mm, using eight sensors or more attain a mean absolute percentage error of less than 1%. The method is also supported by real laboratory measurements using eight sensor positions, where the error is less than 0.7%. Experimental measurements near the pipe end demonstrate that with appropriate positioning, end-reflections should not have a detrimental effect on the accuracy. The method estimates the mean thickness in cases where the pipe wall has near-uniform thickness but may not be as reliable when there are large shallow defects caused by metal loss, which is typically the case when the inner wall has suffered from erosion. In this case, monitoring of the stacking power curves could detect regional metal loss due to erosion and analysis of the local maxima could uncover information about individual thickness segments around the pipe circumference.
Wangensteen, Magnus; Johansen, Tonni Franke; Fatemi, Ali; Viggen, Erlend Magnus; Haugan, Lars Eidissen
Pitting Detection and Characterization From Ultrasound Timelapse Images Using Convolutional Neural Networks Journal Article
In: IEEE Open Journal of Instrumentation and Measurement, 2024.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@article{wangensteen_pitting_2024,
title = {Pitting Detection and Characterization From Ultrasound Timelapse Images Using Convolutional Neural Networks},
author = {Magnus Wangensteen and Tonni Franke Johansen and Ali Fatemi and Erlend Magnus Viggen and Lars Eidissen Haugan},
doi = {10.1109/OJIM.2024.3396829},
year = {2024},
date = {2024-05-06},
journal = {IEEE Open Journal of Instrumentation and Measurement},
abstract = {Pitting corrosion, a localized form of corrosion leading to cavities and structural failure in metallic materials, requires early detection for effective mitigation. While ultrasonic inspection techniques can readily detect uniform wall thinning, they often struggle to identify pitting corrosion. This study proposes a time-lapse ultrasound inspection method to detect early-stage pitting using pulse-echo sensors. By recording multiple ultrasonic traces over time, 2D timelapse images of ultrasonic reflectivity can be generated and fed into a trained neural network for pitting diagnostics. In general, training a machine learning model requires a large training dataset. This work used data from a drilling experiment to generate a suitable dataset. Dataset construction by random time-ordered combinations of ultrasonic measurements was conducted to create a diverse set of time-lapse image samples to generalize the resulting machine-learning model adequately. A classification neural network was trained to detect the presence of drilled holes, and a separate regression network was trained to estimate the hole depth. Based on drilling data from an independently acquired test dataset, results demonstrate a mean absolute error of 0.163 mm for hole depth estimations. All holes are successfully detected when 0.1 mm deeper than the defined pitting threshold of 0.5 mm. This suggests that the proposed method generalizes well and can be deployed to any similar acquisition system.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {article}
}
Pitting corrosion, a localized form of corrosion leading to cavities and structural failure in metallic materials, requires early detection for effective mitigation. While ultrasonic inspection techniques can readily detect uniform wall thinning, they often struggle to identify pitting corrosion. This study proposes a time-lapse ultrasound inspection method to detect early-stage pitting using pulse-echo sensors. By recording multiple ultrasonic traces over time, 2D timelapse images of ultrasonic reflectivity can be generated and fed into a trained neural network for pitting diagnostics. In general, training a machine learning model requires a large training dataset. This work used data from a drilling experiment to generate a suitable dataset. Dataset construction by random time-ordered combinations of ultrasonic measurements was conducted to create a diverse set of time-lapse image samples to generalize the resulting machine-learning model adequately. A classification neural network was trained to detect the presence of drilled holes, and a separate regression network was trained to estimate the hole depth. Based on drilling data from an independently acquired test dataset, results demonstrate a mean absolute error of 0.163 mm for hole depth estimations. All holes are successfully detected when 0.1 mm deeper than the defined pitting threshold of 0.5 mm. This suggests that the proposed method generalizes well and can be deployed to any similar acquisition system.
Viggen, Erlend Magnus (Ed.)
Proceedings of the 47th Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2024, ISBN: 978-82-8123-024-8.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2024,
title = {Proceedings of the 47th Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/380743405_Proceedings_of_the_47th_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-024-8},
year = {2024},
date = {2024-05-01},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Viggen, Erlend Magnus; Diez, Anja; Johansen, Tonni Franke
Pyintegrity: An Open-Source Toolbox for Processing Ultrasonic Pulse-Echo Well Integrity Log Data Proceedings Article
In: SPE Norway Subsurface Conference, pp. 12, Society of Petroleum Engineers, 2024, ISBN: 978-1-959025-34-4.
Abstract | BibTeX | Tags: well logging | Links:
@inproceedings{viggen_pyintegrity_2024,
title = {Pyintegrity: An Open-Source Toolbox for Processing Ultrasonic Pulse-Echo Well Integrity Log Data},
author = {Erlend Magnus Viggen and Anja Diez and Tonni Franke Johansen},
doi = {10.2118/218476-MS},
isbn = {978-1-959025-34-4},
year = {2024},
date = {2024-04-17},
urldate = {2024-04-17},
booktitle = {SPE Norway Subsurface Conference},
pages = {12},
publisher = {Society of Petroleum Engineers},
abstract = {Many companies offer similarly designed wireline tools using ultrasonic pulse-echo measurements to evaluate barrier integrity in cased-hole wells. While these tools provide very similar data, different companies process their data using different algorithms, typically to estimate the pipe wall thickness and the outer material's acoustic impedance. While the algorithms themselves are public, no openly available software implementations are available. Therefore, we have developed an open-source software toolbox called Pyintegrity implementing many of these algorithms. In this article, we demonstrate Pyintegrity by applying its algorithm implementations to a well integrity log from the open Volve Data Village dataset. Our results demonstrate that it is quite possible to process data recorded by a particular tool using processing algorithms developed for use with other similar tools, and we find a good correspondence between the different processing algorithms. Comparing the results produced by the different processing algorithms lets us confidently identify certain features in some of the results as processing artifacts that do not reflect the physical state of the well.},
keywords = {well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
Many companies offer similarly designed wireline tools using ultrasonic pulse-echo measurements to evaluate barrier integrity in cased-hole wells. While these tools provide very similar data, different companies process their data using different algorithms, typically to estimate the pipe wall thickness and the outer material's acoustic impedance. While the algorithms themselves are public, no openly available software implementations are available. Therefore, we have developed an open-source software toolbox called Pyintegrity implementing many of these algorithms. In this article, we demonstrate Pyintegrity by applying its algorithm implementations to a well integrity log from the open Volve Data Village dataset. Our results demonstrate that it is quite possible to process data recorded by a particular tool using processing algorithms developed for use with other similar tools, and we find a good correspondence between the different processing algorithms. Comparing the results produced by the different processing algorithms lets us confidently identify certain features in some of the results as processing artifacts that do not reflect the physical state of the well.
Diez, Anja; Viggen, Erlend Magnus; Johansen, Tonni Franke
Open Database of Simulated Ultrasonic Pulse-Echo Well Integrity Log Data Proceedings Article
In: SPE Norway Subsurface Conference, pp. 7, Society of Petroleum Engineers, 2024, ISBN: 978-1-959025-34-4.
Abstract | BibTeX | Tags: well logging | Links:
@inproceedings{diez_open_2024,
title = {Open Database of Simulated Ultrasonic Pulse-Echo Well Integrity Log Data},
author = {Anja Diez and Erlend Magnus Viggen and Tonni Franke Johansen},
doi = {10.2118/218457-MS},
isbn = {978-1-959025-34-4},
year = {2024},
date = {2024-04-17},
booktitle = {SPE Norway Subsurface Conference},
pages = {7},
publisher = {Society of Petroleum Engineers},
abstract = {Pulse echo measurements are used to investigate the conditions on the outside of an oil or gas pipe by sending ultrasound pulses onto the pipe wall from inside the pipe that reverberate within the pipe wall. A range of different algorithms are used today to analyse this data and derive pipe-wall thickness and impedance of the material behind the pipe, with the aim of determining the bonding of the material. To be able to develop current algorithms further it is crucial to understand currently used algorithms and their advantages and disadvantages. The downside with using logging data from real boreholes is that no ground truth exists, making it difficult to evaluate the accuracy of the different algorithms. Therefore, we built a database of numerically generated data. This database allows us to investigate the effects of variations like casing diameter, thickness, bonding, and eccentering on the derived casing thickness and outer- material impedance using different analysis algorithms. Here, we use three of the most used algorithms and discuss comparisons of results gained from the analysis of the simulated data in the case of tool eccentering and existence of a fluid-filled annulus gap between pipe and cement on the outside showing the value of simulated data to improve and understand estimates of pipe thickness and outer-material impedance.},
keywords = {well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
Pulse echo measurements are used to investigate the conditions on the outside of an oil or gas pipe by sending ultrasound pulses onto the pipe wall from inside the pipe that reverberate within the pipe wall. A range of different algorithms are used today to analyse this data and derive pipe-wall thickness and impedance of the material behind the pipe, with the aim of determining the bonding of the material. To be able to develop current algorithms further it is crucial to understand currently used algorithms and their advantages and disadvantages. The downside with using logging data from real boreholes is that no ground truth exists, making it difficult to evaluate the accuracy of the different algorithms. Therefore, we built a database of numerically generated data. This database allows us to investigate the effects of variations like casing diameter, thickness, bonding, and eccentering on the derived casing thickness and outer- material impedance using different analysis algorithms. Here, we use three of the most used algorithms and discuss comparisons of results gained from the analysis of the simulated data in the case of tool eccentering and existence of a fluid-filled annulus gap between pipe and cement on the outside showing the value of simulated data to improve and understand estimates of pipe thickness and outer-material impedance.
Wangensteen, Magnus; Ali Fatemi,; Johansen, Tonni Franke; Viggen, Erlend Magnus
Pitting Corrosion Detection by Ultrasound Monitoring Proceedings Article
In: AMPP CONFERENCE 2024, pp. 11, AMPP, 2024.
Abstract | BibTeX | Tags: acoustics, machine learning | Links:
@inproceedings{wangensteen_2024_pitting,
title = {Pitting Corrosion Detection by Ultrasound Monitoring},
author = {Wangensteen, Magnus and Fatemi, Ali, and Johansen, Tonni Franke and Viggen, Erlend Magnus},
doi = {10.5006/C2024-20810},
year = {2024},
date = {2024-03-03},
booktitle = {AMPP CONFERENCE 2024},
pages = {11},
publisher = {AMPP},
abstract = {arly diagnosis is essential for successful mitigation of pitting corrosion, a localized form of corrosion that causes cavities and structural failure in metallic materials. Although ultrasonic inspection techniques are effective in detecting uniform wall thinning, they have challenges in accurately identifying pitting corrosion.
The present work proposes a technique for early-stage pitting detection utilizing time-lapse pulse-echo signals. The generation of two-dimensional time-lapse images of ultrasonic reflectivity may be achieved by capturing several ultrasonic traces over a period of time. These images can then serve as input for a neural network trained specifically for the purpose of pitting diagnostics.
To obtain a substantial training dataset required for training the machine learning model, a drilling experiment was conducted, and random time-ordered combinations of the pulse-echo measurements produced the time-lapse images. A classification neural network was trained to detect the presence of pits, while a separate regression network was trained to estimate the pit depth.
Test data from a previously unseen transducer indicates that the pit depth estimations exhibit a mean absolute error of less than 0.2 mm. All pits are reliably identified when they exceed the defined pitting threshold of 0.5 mm by a depth of 0.1 mm.},
keywords = {acoustics, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
arly diagnosis is essential for successful mitigation of pitting corrosion, a localized form of corrosion that causes cavities and structural failure in metallic materials. Although ultrasonic inspection techniques are effective in detecting uniform wall thinning, they have challenges in accurately identifying pitting corrosion.
The present work proposes a technique for early-stage pitting detection utilizing time-lapse pulse-echo signals. The generation of two-dimensional time-lapse images of ultrasonic reflectivity may be achieved by capturing several ultrasonic traces over a period of time. These images can then serve as input for a neural network trained specifically for the purpose of pitting diagnostics.
To obtain a substantial training dataset required for training the machine learning model, a drilling experiment was conducted, and random time-ordered combinations of the pulse-echo measurements produced the time-lapse images. A classification neural network was trained to detect the presence of pits, while a separate regression network was trained to estimate the pit depth.
Test data from a previously unseen transducer indicates that the pit depth estimations exhibit a mean absolute error of less than 0.2 mm. All pits are reliably identified when they exceed the defined pitting threshold of 0.5 mm by a depth of 0.1 mm.
The present work proposes a technique for early-stage pitting detection utilizing time-lapse pulse-echo signals. The generation of two-dimensional time-lapse images of ultrasonic reflectivity may be achieved by capturing several ultrasonic traces over a period of time. These images can then serve as input for a neural network trained specifically for the purpose of pitting diagnostics.
To obtain a substantial training dataset required for training the machine learning model, a drilling experiment was conducted, and random time-ordered combinations of the pulse-echo measurements produced the time-lapse images. A classification neural network was trained to detect the presence of pits, while a separate regression network was trained to estimate the pit depth.
Test data from a previously unseen transducer indicates that the pit depth estimations exhibit a mean absolute error of less than 0.2 mm. All pits are reliably identified when they exceed the defined pitting threshold of 0.5 mm by a depth of 0.1 mm.
2023
Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo
Modelling acoustic radiation from vibrating surfaces around coincidence: Radiation into fluids Journal Article
In: Journal of Sound and Vibration, vol. 560, pp. 20, 2023, ISBN: 0022-460X.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@article{viggen_modelling_2023,
title = {Modelling acoustic radiation from vibrating surfaces around coincidence: Radiation into fluids},
author = {Erlend Magnus Viggen and Håvard Kjellmo Arnestad},
doi = {10.1016/j.jsv.2023.117787},
isbn = {0022-460X},
year = {2023},
date = {2023-09-15},
urldate = {2023-09-15},
journal = {Journal of Sound and Vibration},
volume = {560},
pages = {20},
abstract = {It is well-known that vibrating surfaces generate sound waves in adjacent fluids. According to the classical radiation model, the nature of these waves depends on whether the vibration’s phase speed cv is above (supersonic) or below (subsonic) the fluid sound speed cf. The transition between these two domains is known as coincidence. In the supersonic domain, the sound wave radiates into the fluid. In the subsonic domain, the classical model states that the wave becomes evanescent and clings to the surface. In the last 30 years, however, several articles on leaky guided waves have reported radiating waves in the subsonic domain, which is at odds with the classical model. In this article, we investigate an enhanced model for sound radiation near and below coincidence. Unlike the classical model, this model fully respects conservation of energy by balancing the radiated power with power lost from the guided wave underlying the vibration. The model takes into account that this power loss and the consequent attenuation of the surface vibration result in an inhomogeneous radiated sound wave — an effect that cannot be neglected near coincidence. We successfully validate the model against exact solutions for leaky A0 Lamb waves around coincidence. The model can also be used as a perturbation method to predict the attenuation of leaky A0 waves from the properties of free A0 waves, giving more accurate estimates than existing perturbation methods. We further investigate subsonic leaky A0 waves using the enhanced model. Thereby we, for example, explain the peculiar reappearance or persistence of the leaky A0 wave at lower frequencies, an effect brought to attention by previous theoretical studies.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {article}
}
It is well-known that vibrating surfaces generate sound waves in adjacent fluids. According to the classical radiation model, the nature of these waves depends on whether the vibration’s phase speed cv is above (supersonic) or below (subsonic) the fluid sound speed cf. The transition between these two domains is known as coincidence. In the supersonic domain, the sound wave radiates into the fluid. In the subsonic domain, the classical model states that the wave becomes evanescent and clings to the surface. In the last 30 years, however, several articles on leaky guided waves have reported radiating waves in the subsonic domain, which is at odds with the classical model. In this article, we investigate an enhanced model for sound radiation near and below coincidence. Unlike the classical model, this model fully respects conservation of energy by balancing the radiated power with power lost from the guided wave underlying the vibration. The model takes into account that this power loss and the consequent attenuation of the surface vibration result in an inhomogeneous radiated sound wave — an effect that cannot be neglected near coincidence. We successfully validate the model against exact solutions for leaky A0 Lamb waves around coincidence. The model can also be used as a perturbation method to predict the attenuation of leaky A0 waves from the properties of free A0 waves, giving more accurate estimates than existing perturbation methods. We further investigate subsonic leaky A0 waves using the enhanced model. Thereby we, for example, explain the peculiar reappearance or persistence of the leaky A0 wave at lower frequencies, an effect brought to attention by previous theoretical studies.
Viggen, Erlend Magnus; Singstad, Bjørn-Jostein; Time, Eirik; Mishra, Siddharth; Berg, Eirik
Assisted cement log interpretation using machine learning Journal Article
In: SPE Drilling & Completion, vol. 38, iss. 02, pp. 220–234, 2023.
Abstract | BibTeX | Tags: machine learning, well logging | Links:
@article{viggen_assisted_2022,
title = {Assisted cement log interpretation using machine learning},
author = {Erlend Magnus Viggen and Bjørn-Jostein Singstad and Eirik Time and Siddharth Mishra and Eirik Berg},
url = {https://hdl.handle.net/11250/3062647, Post-print at NTNU Open},
doi = {10.2118/209529-PA},
year = {2023},
date = {2023-06-14},
urldate = {2023-06-14},
journal = {SPE Drilling & Completion},
volume = {38},
issue = {02},
pages = {220–234},
abstract = {The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high-quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with cased-hole logging interpretation software. By containerizing the code using an “API First” design principle (API: application programming interface), the applicability of this add-on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the code will be made available as open source.},
keywords = {machine learning, well logging},
pubstate = {published},
tppubtype = {article}
}
The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high-quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with cased-hole logging interpretation software. By containerizing the code using an “API First” design principle (API: application programming interface), the applicability of this add-on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the code will be made available as open source.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high-quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with cased-hole logging interpretation software. By containerizing the code using an “API First” design principle (API: application programming interface), the applicability of this add-on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the code will be made available as open source.
Johansen, Tonni Franke; Buschmann, Philip Erik; Røsberg, Knut Marius; Diez, Anja; Viggen, Erlend Magnus
Ultrasonic well integrity logging using phased array technology Proceedings Article
In: Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, pp. 9, American Society of Mechanical Engineers, 2023, ISBN: 978-0-7918-8691-5.
Abstract | BibTeX | Tags: guided waves, well logging | Links:
@inproceedings{johansen_ultrasonic_2023,
title = {Ultrasonic well integrity logging using phased array technology},
author = {Tonni Franke Johansen and Philip Erik Buschmann and Knut Marius Røsberg and Anja Diez and Erlend Magnus Viggen},
url = {https://hdl.handle.net/11250/3102191, Full-text on NTNU Open},
doi = {10.1115/OMAE2023-108101},
isbn = {978-0-7918-8691-5},
year = {2023},
date = {2023-06-11},
urldate = {2023-06-11},
booktitle = {Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering},
volume = {9},
pages = {9},
publisher = {American Society of Mechanical Engineers},
abstract = {Ultrasonic well integrity logging is an important and common procedure for well completion and plug-and-abandonment operations. Typical logging tools employ single-element ultrasound transducers. In medical ultrasound imaging, however, more flexible phased arrays are the standard. This paper presents a first set of experimental results obtained with up to two linear 32-element phased arrays that are specifically designed for plug-and-abandonment operations in terms of their centre frequency. The experiments encompass pulse-echo and pitch-catch studies for different incidence angles and aperture sizes on plates and pipes with wall thickness as encountered in the offshore industry. The pulse-echo experiment is backed up by simulations, and shows that effect of the incidence angle on the pipe resonance’s frequency and strength is weak and more strong, respectively, and that the effect depends on the frequency response and directivity of the transducer. The pitch-catch experiment demonstrates the importance of carefully choosing the right angle of incidence to excite the intended wave modes.},
keywords = {guided waves, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
Ultrasonic well integrity logging is an important and common procedure for well completion and plug-and-abandonment operations. Typical logging tools employ single-element ultrasound transducers. In medical ultrasound imaging, however, more flexible phased arrays are the standard. This paper presents a first set of experimental results obtained with up to two linear 32-element phased arrays that are specifically designed for plug-and-abandonment operations in terms of their centre frequency. The experiments encompass pulse-echo and pitch-catch studies for different incidence angles and aperture sizes on plates and pipes with wall thickness as encountered in the offshore industry. The pulse-echo experiment is backed up by simulations, and shows that effect of the incidence angle on the pipe resonance’s frequency and strength is weak and more strong, respectively, and that the effect depends on the frequency response and directivity of the transducer. The pitch-catch experiment demonstrates the importance of carefully choosing the right angle of incidence to excite the intended wave modes.
Viggen, Erlend Magnus (Ed.)
Proceedings of the 46th Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2023, ISBN: 978-82-8123-023-1.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2023,
title = {Proceedings of the 46th Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/370074370_Proceedings_of_the_46th_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-023-1},
year = {2023},
date = {2023-04-18},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Diez, Anja; Johansen, Tonni Franke; Viggen, Erlend Magnus
From 3D to 1D: Effective numerical modelling of pulse-echo measurements in pipes Proceedings Article
In: Proceedings of the 46th Scandinavian Symposium on Physical Acoustics, pp. 23, Norwegian Physical Society, 2023, ISBN: 978-82-8123-023-1.
Abstract | BibTeX | Tags: acoustics, well logging | Links:
@inproceedings{diez_from_2023,
title = {From 3D to 1D: Effective numerical modelling of pulse-echo measurements in pipes},
author = {Anja Diez and Tonni Franke Johansen and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/369926875_From_3D_to_1D_Effective_numerical_modelling_of_pulse-echo_measurements_in_pipes, Full-text on ResearchGate},
isbn = {978-82-8123-023-1},
year = {2023},
date = {2023-04-06},
urldate = {2023-04-06},
booktitle = {Proceedings of the 46th Scandinavian Symposium on Physical Acoustics},
pages = {23},
publisher = {Norwegian Physical Society},
abstract = {In the oil and gas industry, pulse echo measurements have for decades been used in cased holes to estimate the properties of the materials on the outside of the pipe. To investigate the methods used to analyse pulse echo measurements from pipes, we use numerical modelling in COMSOL Multiphysics. While 3D models correctly capture the real geometry, they are computationally heavy and, therefore, not appropriate for simulating a large range of geometric and material variations. Analytic 1D plane wave models are fast to calculate, but we observe large deviations between the 3D and 1D results, showing that corrections to 1D model results are necessary. Therefore, we investigate using 2D and axisymmetric 2.5D models instead and find good agreement between the 2.5D and 3D model results and larger deviations between the 2D and 3D model results. Further, we find that the time explicit formulation works reliably, with an effective absorbing layer, while using the time domain formulation requires more care and a larger domain due to the poorer performance of its perfectly matched layer. Nevertheless, the time domain formulation is preferable when introducing thin domains and additional domain boundaries to keep the computational time at an acceptable level.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
In the oil and gas industry, pulse echo measurements have for decades been used in cased holes to estimate the properties of the materials on the outside of the pipe. To investigate the methods used to analyse pulse echo measurements from pipes, we use numerical modelling in COMSOL Multiphysics. While 3D models correctly capture the real geometry, they are computationally heavy and, therefore, not appropriate for simulating a large range of geometric and material variations. Analytic 1D plane wave models are fast to calculate, but we observe large deviations between the 3D and 1D results, showing that corrections to 1D model results are necessary. Therefore, we investigate using 2D and axisymmetric 2.5D models instead and find good agreement between the 2.5D and 3D model results and larger deviations between the 2D and 3D model results. Further, we find that the time explicit formulation works reliably, with an effective absorbing layer, while using the time domain formulation requires more care and a larger domain due to the poorer performance of its perfectly matched layer. Nevertheless, the time domain formulation is preferable when introducing thin domains and additional domain boundaries to keep the computational time at an acceptable level.
Wangensteen, Magnus; Johansen, Tonni Franke; Fatemi, Ali; Viggen, Erlend Magnus
Ultrasonic Guided Waves and Machine Learning for Corrosion Monitoring in Steel Pipes Proceedings Article
In: AMPP Annual Conference + Expo, pp. 12, Association for Materials Protection and Performance, 2023.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@inproceedings{wangensteen_ultrasonic_2023,
title = {Ultrasonic Guided Waves and Machine Learning for Corrosion Monitoring in Steel Pipes},
author = {Magnus Wangensteen and Tonni Franke Johansen and Ali Fatemi and Erlend Magnus Viggen},
url = {https://onepetro.org/amppcorr/proceedings/AMPP23/All-AMPP23/AMPP-2023-19289/527075, Paper on OnePetro},
year = {2023},
date = {2023-03-19},
urldate = {2023-03-19},
booktitle = {AMPP Annual Conference + Expo},
pages = {12},
publisher = {Association for Materials Protection and Performance},
abstract = {In a pipe, a circumferentially travelling ultrasonic wave will gather information about the properties and boundaries of the propagation medium. However, the compounded effects of diagnostic features like mean pipe wall thinning, surface roughness, regional depressions, and pit developments are difficult to separate using traditional methods. Therefore, this study proposes an approach using artificial neural networks to estimate the diagnostic features of interest.
This study is based on ultrasound simulations and synthetic data. The synthetic data is recorded at a set of transducer positions at the outer pipe wall. The resulting traces are then combined into 2D images where each vertical line represents the waveform recorded at a specific transducer location. The resulting images are used to train a neural network to extract relevant features.
Diagnostic features for mean and minimum thickness, as well as standard deviation of the wall thickness, are quite accurately estimated. The neural network-based estimation for mean thickness is more accurate than a conventional reference method. This is observed especially for non-uniform wall thickness, which is typically the case if the pipe wall has been exposed to erosion and corrosion. Features for depth and location of depressions are also informative but less accurate. Data decimation experiments have also been conducted, even down to one single remaining trace. Also in this case, the neural network is able to make good estimates of some features, especially the mean wall thickness.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {inproceedings}
}
In a pipe, a circumferentially travelling ultrasonic wave will gather information about the properties and boundaries of the propagation medium. However, the compounded effects of diagnostic features like mean pipe wall thinning, surface roughness, regional depressions, and pit developments are difficult to separate using traditional methods. Therefore, this study proposes an approach using artificial neural networks to estimate the diagnostic features of interest.
This study is based on ultrasound simulations and synthetic data. The synthetic data is recorded at a set of transducer positions at the outer pipe wall. The resulting traces are then combined into 2D images where each vertical line represents the waveform recorded at a specific transducer location. The resulting images are used to train a neural network to extract relevant features.
Diagnostic features for mean and minimum thickness, as well as standard deviation of the wall thickness, are quite accurately estimated. The neural network-based estimation for mean thickness is more accurate than a conventional reference method. This is observed especially for non-uniform wall thickness, which is typically the case if the pipe wall has been exposed to erosion and corrosion. Features for depth and location of depressions are also informative but less accurate. Data decimation experiments have also been conducted, even down to one single remaining trace. Also in this case, the neural network is able to make good estimates of some features, especially the mean wall thickness.
This study is based on ultrasound simulations and synthetic data. The synthetic data is recorded at a set of transducer positions at the outer pipe wall. The resulting traces are then combined into 2D images where each vertical line represents the waveform recorded at a specific transducer location. The resulting images are used to train a neural network to extract relevant features.
Diagnostic features for mean and minimum thickness, as well as standard deviation of the wall thickness, are quite accurately estimated. The neural network-based estimation for mean thickness is more accurate than a conventional reference method. This is observed especially for non-uniform wall thickness, which is typically the case if the pipe wall has been exposed to erosion and corrosion. Features for depth and location of depressions are also informative but less accurate. Data decimation experiments have also been conducted, even down to one single remaining trace. Also in this case, the neural network is able to make good estimates of some features, especially the mean wall thickness.
2022
Arnestad, Håvard Kjellmo; Viggen, Erlend Magnus
A fast simulation method for Lamb wave propagation in coupled non-parallel plates Presentation
Poster presented at the IEEE International Ultrasonics Symposium 2022, 12.10.2022.
Abstract | BibTeX | Tags: acoustics, guided waves, well logging | Links:
@misc{nokey,
title = {A fast simulation method for Lamb wave propagation in coupled non-parallel plates},
author = {Håvard Kjellmo Arnestad and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/364359505_A_fast_simulation_method_for_Lamb_wave_propagation_in_coupled_non-parallel_plates, Poster on ResearchGate},
year = {2022},
date = {2022-10-12},
urldate = {2022-10-12},
abstract = {Some systems in ultrasonic testing can be approximated as two non-parallel plates coupled by a fluid, where leaky Lamb waves propagate in each plate. This work develops a fast and accurate simulation method for such systems by extending methods based on the theory of layered media to non-parallel surfaces. The aim is to determine the presence of cement through two steel plates via inversion. The method runs roughly 10 000 times faster than equivalent simulations in COMSOL. Three-dimensional propagation is also shown, and a mechanism based on Lamb mode conversion between tilted plates is explained.},
howpublished = {Poster presented at the IEEE International Ultrasonics Symposium 2022},
keywords = {acoustics, guided waves, well logging},
pubstate = {published},
tppubtype = {presentation}
}
Some systems in ultrasonic testing can be approximated as two non-parallel plates coupled by a fluid, where leaky Lamb waves propagate in each plate. This work develops a fast and accurate simulation method for such systems by extending methods based on the theory of layered media to non-parallel surfaces. The aim is to determine the presence of cement through two steel plates via inversion. The method runs roughly 10 000 times faster than equivalent simulations in COMSOL. Three-dimensional propagation is also shown, and a mechanism based on Lamb mode conversion between tilted plates is explained.
Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo
An explanatory model for sound radiation from subsonic surface vibrations Presentation
Poster presented at the IEEE International Ultrasonics Symposium 2022, 11.10.2022.
Abstract | BibTeX | Tags: acoustics | Links:
@misc{viggen_explanatory_2022,
title = {An explanatory model for sound radiation from subsonic surface vibrations},
author = {Erlend Magnus Viggen and Håvard Kjellmo Arnestad},
url = {https://www.researchgate.net/publication/364346895_An_explanatory_model_for_sound_radiation_from_subsonic_surface_vibrations, Poster on ResearchGate
https://www.youtube.com/watch?v=yx0Hu-Jjw3w, Poster summary video on YouTube},
year = {2022},
date = {2022-10-11},
urldate = {2022-10-11},
abstract = {Surface vibrations will generate pressure waves in an adjacent fluid. It is commonly held that these waves radiate away from the surface only if the vibration is supersonic, i.e., faster than the fluid's sound speed. However, multiple articles have shown mathematically that even subsonic vibrations can yield radiating waves. Even so, the physical understanding of this phenomenon has been insufficient. In our work, we derive and validate an explanatory physical model to provide such an understanding.},
howpublished = {Poster presented at the IEEE International Ultrasonics Symposium 2022},
keywords = {acoustics},
pubstate = {published},
tppubtype = {presentation}
}
Surface vibrations will generate pressure waves in an adjacent fluid. It is commonly held that these waves radiate away from the surface only if the vibration is supersonic, i.e., faster than the fluid's sound speed. However, multiple articles have shown mathematically that even subsonic vibrations can yield radiating waves. Even so, the physical understanding of this phenomenon has been insufficient. In our work, we derive and validate an explanatory physical model to provide such an understanding.
Viggen, Erlend Magnus (Ed.)
Proceedings of the 45th Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2022, ISBN: 978-82-8123-022-4.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2022,
title = {Proceedings of the 45th Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/360316947_Proceedings_of_the_45th_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-022-4},
year = {2022},
date = {2022-05-02},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo
Inhomogeneous P- and S-wavefields radiated into isotropic elastic solids Proceedings Article
In: Proceedings of the 45th Scandinavian Symposium on Physical Acoustics, pp. 13, Norwegian Physical Society, Online, 2022.
Abstract | BibTeX | Tags: acoustics | Links:
@inproceedings{viggen_inhomogeneous_2022,
title = {Inhomogeneous P- and S-wavefields radiated into isotropic elastic solids},
author = {Erlend Magnus Viggen and Håvard Kjellmo Arnestad},
url = {https://www.researchgate.net/publication/360217948_Inhomogeneous_P-_and_S-wavefields_radiated_into_isotropic_elastic_solids, Full-text on ResearchGate},
year = {2022},
date = {2022-05-02},
urldate = {2022-05-02},
booktitle = {Proceedings of the 45th Scandinavian Symposium on Physical Acoustics},
pages = {13},
publisher = {Norwegian Physical Society},
address = {Online},
abstract = {A vibrating surface in contact with a solid material will generate P- and S-waves in the solid. When the surface vibration is spatially attenuated, we must take into account that the generated waves are always inhomogeneous. In an isotropic elastic solid, such inhomogeneous waves are attenuated perpendicularly to their direction of propagation. When the surface vibration’s phase speed is lower than the Pand/or S-waves’ speed of sound, the inhomogeneity affects the radiation of P- and S-waves in a major but relatively poorly understood way. For a better understanding, finding the total radiated intensity of the two inhomogeneous waves is key. Our work takes a step towards such an understanding by deriving analytical expressions for the velocity, strain, stress, and intensity fields of arbitrarily inhomogeneous Pand S-waves. Furthermore, we investigate whether the total radiated intensity can be found as the sum of the intensities of the individual P- and S-waves. We find that this is only possible when the surface vibration is unattenuated; for attenuated vibrations, the total radiated intensity should be calculated numerically.},
keywords = {acoustics},
pubstate = {published},
tppubtype = {inproceedings}
}
A vibrating surface in contact with a solid material will generate P- and S-waves in the solid. When the surface vibration is spatially attenuated, we must take into account that the generated waves are always inhomogeneous. In an isotropic elastic solid, such inhomogeneous waves are attenuated perpendicularly to their direction of propagation. When the surface vibration’s phase speed is lower than the Pand/or S-waves’ speed of sound, the inhomogeneity affects the radiation of P- and S-waves in a major but relatively poorly understood way. For a better understanding, finding the total radiated intensity of the two inhomogeneous waves is key. Our work takes a step towards such an understanding by deriving analytical expressions for the velocity, strain, stress, and intensity fields of arbitrarily inhomogeneous Pand S-waves. Furthermore, we investigate whether the total radiated intensity can be found as the sum of the intensities of the individual P- and S-waves. We find that this is only possible when the surface vibration is unattenuated; for attenuated vibrations, the total radiated intensity should be calculated numerically.
Time, Eirik; Viggen, Erlend Magnus; Mishra, Siddharth; Berg, Eirik
Assisted cement log interpretation Proceedings Article
In: SPE Norway Subsurface Conference 2022, pp. 15, Society of Petroleum Engineers, 2022.
Abstract | BibTeX | Tags: machine learning, well logging | Links:
@inproceedings{time_assisted_2022,
title = {Assisted cement log interpretation},
author = {Eirik Time and Erlend Magnus Viggen and Siddharth Mishra and Eirik Berg},
doi = {10.2118/209529-MS},
year = {2022},
date = {2022-04-27},
urldate = {2022-04-27},
booktitle = {SPE Norway Subsurface Conference 2022},
pages = {15},
publisher = {Society of Petroleum Engineers},
abstract = {The Assisted Cement Log Interpretation project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals in the cased hole logging unit. By using high quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with the cased hole logging interpretation software. By containerizing the code using an "API First" design principle, the applicability of this add- on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the results are made available as open source.},
keywords = {machine learning, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
The Assisted Cement Log Interpretation project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals in the cased hole logging unit. By using high quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with the cased hole logging interpretation software. By containerizing the code using an "API First" design principle, the applicability of this add- on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the results are made available as open source.
The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals in the cased hole logging unit. By using high quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.
The tool consists of a training and a prediction tool integrated with the cased hole logging interpretation software. By containerizing the code using an "API First" design principle, the applicability of this add- on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.
To improve cement log interpretation consistency in the industry, the results are made available as open source.
2021
Viggen, Erlend Magnus; Løvstakken, Lasse; Måsøy, Svein-Erik; Merciu, Ioan Alexandru
Better automatic interpretation of cement evaluation logs through feature engineering Journal Article
In: SPE Journal, vol. 26, no. 05, pp. 2894–2913, 2021, ISSN: 1930-0220.
Abstract | BibTeX | Tags: machine learning, well logging | Links:
@article{viggen_better_2021b,
title = {Better automatic interpretation of cement evaluation logs through feature engineering},
author = {Erlend Magnus Viggen and Lasse Løvstakken and Svein-Erik Måsøy and Ioan Alexandru Merciu},
doi = {10.2118/204057-PA},
issn = {1930-0220},
year = {2021},
date = {2021-10-13},
urldate = {2021-10-13},
journal = {SPE Journal},
volume = {26},
number = {05},
pages = {2894–2913},
abstract = {We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches, one previously published and one new. The previous approach is based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, whereas the new approach uses feature engineering, in which we use our own domain knowledge to extract features.
We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performingfeature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity.
This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.},
keywords = {machine learning, well logging},
pubstate = {published},
tppubtype = {article}
}
We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches, one previously published and one new. The previous approach is based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, whereas the new approach uses feature engineering, in which we use our own domain knowledge to extract features.
We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performingfeature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity.
This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.
We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performingfeature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity.
This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.
Viggen, Erlend Magnus (Ed.)
Proceedings of the 44th Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2021, ISBN: 978-82-8123-021-7.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2021,
title = {Proceedings of the 44th Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/351233812_Proceedings_of_the_44th_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-021-7},
year = {2021},
date = {2021-04-30},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo
Understanding sound radiation from surface vibrations moving at subsonic speeds Proceedings Article
In: Proceedings of the 44th Scandinavian Symposium on Physical Acoustics, pp. 4, Norwegian Physical Society, Online, 2021, ISBN: 978-82-8123-021-71, (Extended abstract).
BibTeX | Tags: acoustics | Links:
@inproceedings{viggen_understanding_2021,
title = {Understanding sound radiation from surface vibrations moving at subsonic speeds},
author = {Erlend Magnus Viggen and Håvard Kjellmo Arnestad},
url = {https://www.researchgate.net/publication/351004418_Understanding_sound_radiation_from_surface_vibrations_moving_at_subsonic_speeds, Full-text on ResearchGate},
isbn = {978-82-8123-021-71},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {Proceedings of the 44th Scandinavian Symposium on Physical Acoustics},
pages = {4},
publisher = {Norwegian Physical Society},
address = {Online},
note = {Extended abstract},
keywords = {acoustics},
pubstate = {published},
tppubtype = {inproceedings}
}
Arnestad, Håvard Kjellmo; Viggen, Erlend Magnus
A fast semi-analytical method for propagating leaky Lamb wavefields Proceedings Article
In: Proceedings of the 44th Scandinavian Symposium on Physical Acoustics, pp. 22, Norwegian Physical Society, Online, 2021, ISBN: 978-82-8123-021-71.
Abstract | BibTeX | Tags: acoustics, guided waves | Links:
@inproceedings{arnestad_fast_2021,
title = {A fast semi-analytical method for propagating leaky Lamb wavefields},
author = {Håvard Kjellmo Arnestad and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/351005828_A_fast_semi-analytical_method_for_propagating_leaky_Lamb_wavefields, Full-text on ResearchGate},
isbn = {978-82-8123-021-71},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {Proceedings of the 44th Scandinavian Symposium on Physical Acoustics},
pages = {22},
publisher = {Norwegian Physical Society},
address = {Online},
abstract = {A fast method is presented for calculating the wavefields from initialized leaky Lamb waves on plates immersed in sufficiently light fluids. The method works by precomputing the dispersion relation and attenuation, and propagating the wavefields in the frequency domain. An angular spectrum approach is used to include leakage into surrounding fluid. Compared to matching FEM simulations, the computations are performed in the order of seconds, rather than hours. The method also benefits from being much easier to set up correctly, but is on the other hand less general in that it cannot handle e.g. scattering from defects. The correspondence is shown to be good for the case of interest.},
keywords = {acoustics, guided waves},
pubstate = {published},
tppubtype = {inproceedings}
}
A fast method is presented for calculating the wavefields from initialized leaky Lamb waves on plates immersed in sufficiently light fluids. The method works by precomputing the dispersion relation and attenuation, and propagating the wavefields in the frequency domain. An angular spectrum approach is used to include leakage into surrounding fluid. Compared to matching FEM simulations, the computations are performed in the order of seconds, rather than hours. The method also benefits from being much easier to set up correctly, but is on the other hand less general in that it cannot handle e.g. scattering from defects. The correspondence is shown to be good for the case of interest.
Estuariwinarno, Mikael Yuan; Viggen, Erlend Magnus
Determining inner geometry properties from eccentered pulse-echo measurements in a pipe Proceedings Article
In: Proceedings of the 44th Scandinavian Symposium on Physical Acoustics, pp. 23, Norwegian Physical Society, Online, 2021, ISBN: 978-82-8123-021-71.
Abstract | BibTeX | Tags: acoustics, well logging | Links:
@inproceedings{estuariwinarno_determining_2021,
title = {Determining inner geometry properties from eccentered pulse-echo measurements in a pipe},
author = {Mikael Yuan Estuariwinarno and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/351064881_Determining_Inner_Geometry_Properties_From_Eccentered_Pulse-Echo_Measurements_in_a_Pipe, Full-text on ResearchGate},
isbn = {978-82-8123-021-71},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {Proceedings of the 44th Scandinavian Symposium on Physical Acoustics},
pages = {23},
publisher = {Norwegian Physical Society},
address = {Online},
abstract = {In the petroleum industry, well integrity evaluation is an essential part of maintaining the safety and sustainability of hydrocarbon production. Ultrasonic pulse-echo cased hole logging is a widely used type of measurement for well integrity evaluation. It gives insight on casing condition and cement quality through the use of an ultrasonic transducer that ideally rotates around the center of the casing. One of the outputs of this logging is a set of inner geometry properties that describe the position of the tool and the inner radius of the casing. However, inner geometry determination is not straightforward as it has to consider the influence of tool eccentering due to gravity and tool movement, which causes the tool to rotate around another axis than the casing center. Despite its importance and wide implementation, detailed information on inner geometry determination from eccentered measurements has not been published in the scientific literature. In this study, an inner geometry determination algorithm was developed and tested on ultrasonic well log data from from the Norwegian North Sea. This algorithm estimates the inner geometry properties, i.e. the tool eccentering properties and the casing inner radius. The results show that the algorithm produces results that give a good match with the results of a reference algorithm from a service company. Our algorithm is also able to handle poor travel time measurements in a more reliable way than the reference algorithm. Hence, this article attempts to enhance and spread the knowledge of ultrasonic cased hole logging, specifically in terms of the determination of casing inner geometry.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
In the petroleum industry, well integrity evaluation is an essential part of maintaining the safety and sustainability of hydrocarbon production. Ultrasonic pulse-echo cased hole logging is a widely used type of measurement for well integrity evaluation. It gives insight on casing condition and cement quality through the use of an ultrasonic transducer that ideally rotates around the center of the casing. One of the outputs of this logging is a set of inner geometry properties that describe the position of the tool and the inner radius of the casing. However, inner geometry determination is not straightforward as it has to consider the influence of tool eccentering due to gravity and tool movement, which causes the tool to rotate around another axis than the casing center. Despite its importance and wide implementation, detailed information on inner geometry determination from eccentered measurements has not been published in the scientific literature. In this study, an inner geometry determination algorithm was developed and tested on ultrasonic well log data from from the Norwegian North Sea. This algorithm estimates the inner geometry properties, i.e. the tool eccentering properties and the casing inner radius. The results show that the algorithm produces results that give a good match with the results of a reference algorithm from a service company. Our algorithm is also able to handle poor travel time measurements in a more reliable way than the reference algorithm. Hence, this article attempts to enhance and spread the knowledge of ultrasonic cased hole logging, specifically in terms of the determination of casing inner geometry.
Viggen, Erlend Magnus; Løvstakken, Lasse; Merciu, Ioan Alexandru; Måsøy, Svein-Erik
Better automatic interpretation of cement evaluation logs through feature engineering Proceedings Article
In: SPE/IADC International Drilling Conference and Exhibition, pp. 28, 2021.
Abstract | BibTeX | Tags: machine learning, well logging | Links:
@inproceedings{viggen_better_2021a,
title = {Better automatic interpretation of cement evaluation logs through feature engineering},
author = {Erlend Magnus Viggen and Lasse Løvstakken and Ioan Alexandru Merciu and Svein-Erik Måsøy},
doi = {10.2118/204057-MS},
year = {2021},
date = {2021-03-09},
urldate = {2021-03-09},
booktitle = {SPE/IADC International Drilling Conference and Exhibition},
pages = {28},
abstract = {We build systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches: A previously published approach based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, and a feature-engineering approach where we use our own domain knowledge to extract features.
We base this work on a dataset of around 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (6 ordinal classes) and hydraulic isolation (2 classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For bond quality, the CNNs’ interpretation exactly matches the reference 51.6% of the time. 88.5% of the time, it does not miss by more than one class. For hydraulic isolation, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4%, 89.5%, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this dataset may be particularly liable to overfitting. This may favour approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalisable. It may also favour simpler classifiers with less overfitting capacity.
This article shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are relatively easy to apply and deploy, with better results than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.},
keywords = {machine learning, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
We build systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches: A previously published approach based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, and a feature-engineering approach where we use our own domain knowledge to extract features.
We base this work on a dataset of around 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (6 ordinal classes) and hydraulic isolation (2 classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For bond quality, the CNNs’ interpretation exactly matches the reference 51.6% of the time. 88.5% of the time, it does not miss by more than one class. For hydraulic isolation, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4%, 89.5%, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this dataset may be particularly liable to overfitting. This may favour approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalisable. It may also favour simpler classifiers with less overfitting capacity.
This article shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are relatively easy to apply and deploy, with better results than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.
We base this work on a dataset of around 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (6 ordinal classes) and hydraulic isolation (2 classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.
For bond quality, the CNNs’ interpretation exactly matches the reference 51.6% of the time. 88.5% of the time, it does not miss by more than one class. For hydraulic isolation, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4%, 89.5%, and 88.9%, respectively.
Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this dataset may be particularly liable to overfitting. This may favour approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalisable. It may also favour simpler classifiers with less overfitting capacity.
This article shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are relatively easy to apply and deploy, with better results than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.
2020
Viggen, Erlend Magnus; Merciu, Ioan Alexandru; Løvstakken, Lasse; Måsøy, Svein-Erik
Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks Journal Article
In: Journal of Petroleum Science and Engineering, vol. 195, pp. 17, 2020, ISSN: 0920-4105.
Abstract | BibTeX | Tags: machine learning, well logging | Links:
@article{viggen_automatic_2020,
title = {Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks},
author = {Erlend Magnus Viggen and Ioan Alexandru Merciu and Lasse Løvstakken and Svein-Erik Måsøy},
url = {https://www.sciencedirect.com/science/article/pii/S0920410520306100},
doi = {10.1016/j.petrol.2020.107539},
issn = {0920-4105},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Journal of Petroleum Science and Engineering},
volume = {195},
pages = {17},
abstract = {The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1 m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference.},
keywords = {machine learning, well logging},
pubstate = {published},
tppubtype = {article}
}
The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1 m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference.
Viggen, Erlend Magnus (Ed.)
Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2020, ISBN: 978-82-8123-020-0.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2020,
title = {Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/341043518_Proceedings_of_the_43rd_Scandinavian_Symposium_on_Physical_Acoustics, Link to proceedings},
isbn = {978-82-8123-020-0},
year = {2020},
date = {2020-04-30},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Viggen, Erlend Magnus; Hårstad, Erlend; Kvalsvik, Jørgen
Getting started with acoustic well log data using the dlisio Python library on the Volve Data Village dataset Proceedings Article
In: Viggen, Erlend Magnus; Hoff, Lars (Ed.): Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics, pp. 36, Norwegian Physical Society, Geilo, Norway, 2020, ISBN: 978-82-8123-020-0.
Abstract | BibTeX | Tags: acoustics, well logging | Links:
@inproceedings{viggen_getting_2020,
title = {Getting started with acoustic well log data using the dlisio Python library on the Volve Data Village dataset},
author = {Erlend Magnus Viggen and Erlend Hårstad and Jørgen Kvalsvik},
editor = {Erlend Magnus Viggen and Lars Hoff},
url = {https://www.researchgate.net/publication/340645995_Getting_started_with_acoustic_well_log_data_using_the_dlisio_Python_library_on_the_Volve_Data_Village_dataset, Full-text on ResearchGate
https://github.com/equinor/dlisio-notebooks/blob/master/acoustic.ipynb, Companion Jupyter Notebook},
isbn = {978-82-8123-020-0},
year = {2020},
date = {2020-04-15},
urldate = {2020-04-15},
booktitle = {Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics},
pages = {36},
publisher = {Norwegian Physical Society},
address = {Geilo, Norway},
abstract = {Three issues have long impeded academic research and teaching on well logging. First, real measured data has been hard to come by. This has now been alleviated by Equinor's 2018 release of the Volve Data Village dataset. Among its 5 TB of data, it contains 16.3 GB of various well log data, plots, and analyses. Second, no free and effective software tools to programmatically read DLIS files, one of the most common file formats for well log data today and by far the most common format in the Volve Data Village, have been available. This has now been remedied by the free and open-source Python library dlisio, first released by Equinor in 2018 and still under heavy development. Third, the data is often difficult to understand, as sufficient documentation is often not publicly available. As different tools measure, process, and store their data differently, different tools must be understood individually. This article aims to stimulate research into well logging, by showing how to use dlisio to investigate well log data from the Volve Data Village dataset. While the investigative methods used here can be adapted to other kinds of data, this article focuses on acoustic integrity logs. Specifically, we investigate data from a sonic tool (DSLT) and an ultrasonic tool (USIT), both extensively used in the dataset. In addition to identifying what the most fundamental pieces of data represent, we also show some simple examples of how this data can be reprocessed to find new results not provided in the well log file. We provide the code underlying this article in an accompanying Jupyter Notebook.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {inproceedings}
}
Three issues have long impeded academic research and teaching on well logging. First, real measured data has been hard to come by. This has now been alleviated by Equinor's 2018 release of the Volve Data Village dataset. Among its 5 TB of data, it contains 16.3 GB of various well log data, plots, and analyses. Second, no free and effective software tools to programmatically read DLIS files, one of the most common file formats for well log data today and by far the most common format in the Volve Data Village, have been available. This has now been remedied by the free and open-source Python library dlisio, first released by Equinor in 2018 and still under heavy development. Third, the data is often difficult to understand, as sufficient documentation is often not publicly available. As different tools measure, process, and store their data differently, different tools must be understood individually. This article aims to stimulate research into well logging, by showing how to use dlisio to investigate well log data from the Volve Data Village dataset. While the investigative methods used here can be adapted to other kinds of data, this article focuses on acoustic integrity logs. Specifically, we investigate data from a sonic tool (DSLT) and an ultrasonic tool (USIT), both extensively used in the dataset. In addition to identifying what the most fundamental pieces of data represent, we also show some simple examples of how this data can be reprocessed to find new results not provided in the well log file. We provide the code underlying this article in an accompanying Jupyter Notebook.
2019
Viggen, Erlend Magnus; Hoff, Lars (Ed.)
Proceedings of the 42nd Scandinavian Symposium on Physical Acoustics Book
Norwegian Physical Society, 2019, ISBN: 978-82-8123-019-4.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2019,
title = {Proceedings of the 42nd Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen and Lars Hoff},
url = {https://arxiv.org/html/1904.12488, Link to proceedings},
isbn = {978-82-8123-019-4},
year = {2019},
date = {2019-05-23},
publisher = {Norwegian Physical Society},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
2018
Gelderblom, Femke B.; Tronstad, Tron V.; Viggen, Erlend Magnus
Subjective evaluation of a noise-reduced training target for deep neural network-based speech enhancement Journal Article
In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 3, pp. 583–594, 2018, ISSN: 2329-9290.
Abstract | BibTeX | Tags: machine learning, speech enhancement | Links:
@article{gelderblom_subjective_2018,
title = {Subjective evaluation of a noise-reduced training target for deep neural network-based speech enhancement},
author = {Femke B. Gelderblom and Tron V. Tronstad and Erlend Magnus Viggen},
url = {https://erlend-viggen.no/wp-content/uploads/2018/11/gelderblom_subjective_2018_post-print.pdf, Full-text},
doi = {10.1109/TASLP.2018.2882738},
issn = {2329-9290},
year = {2018},
date = {2018-11-21},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume = {27},
number = {3},
pages = {583–594},
abstract = {Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of both systems was evaluated subjectively, by a Speech Recognition Test based on Hagerman sentences and a translation of the ITU-T P.835 recommendation, respectively. Results were compared with the objective measures STOI and POLQA. Neither STOI nor POLQA reliably predicted subjective results. While STOI anticipated improvement, subjective results for both models showed degradation of speech intelligibility. POLQA results were overall hardly affected, while the subjective results showed significant changes in overall quality, both positive and negative, in many of the tests. One of the systems was trained to remove all noise; a strategy that is common in speech enhancement systems found in the literature. The other system was trained to only reduce the noise such that the signal-to-noise ratio increased with 10 dB. The latter system subjectively outperformed the system that attempted to remove noise completely. From this, we conclude that objective evaluation cannot replace subjective evaluation until a measure that reliably predicts intelligibility and quality for deep neural network based systems has been identified. Results further indicate that it may be beneficial to move away from more aggressive noise removal strategies towards noise reduction strategies that cause less speech distortion.},
keywords = {machine learning, speech enhancement},
pubstate = {published},
tppubtype = {article}
}
Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of both systems was evaluated subjectively, by a Speech Recognition Test based on Hagerman sentences and a translation of the ITU-T P.835 recommendation, respectively. Results were compared with the objective measures STOI and POLQA. Neither STOI nor POLQA reliably predicted subjective results. While STOI anticipated improvement, subjective results for both models showed degradation of speech intelligibility. POLQA results were overall hardly affected, while the subjective results showed significant changes in overall quality, both positive and negative, in many of the tests. One of the systems was trained to remove all noise; a strategy that is common in speech enhancement systems found in the literature. The other system was trained to only reduce the noise such that the signal-to-noise ratio increased with 10 dB. The latter system subjectively outperformed the system that attempted to remove noise completely. From this, we conclude that objective evaluation cannot replace subjective evaluation until a measure that reliably predicts intelligibility and quality for deep neural network based systems has been identified. Results further indicate that it may be beneficial to move away from more aggressive noise removal strategies towards noise reduction strategies that cause less speech distortion.
2017
Gelderblom, Femke B.; Tronstad, Tron V.; Viggen, Erlend Magnus
Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement Presentation
22.08.2017.
BibTeX | Tags: machine learning, speech enhancement | Links:
@misc{gelderblom_subjective_2017b,
title = {Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement},
author = {Femke B. Gelderblom and Tron V. Tronstad and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/319243091_Poster_Subjective_intelligibility_of_deep_neural_network-based_speech_enhancement, Poster on ResearchGate},
year = {2017},
date = {2017-08-22},
keywords = {machine learning, speech enhancement},
pubstate = {published},
tppubtype = {presentation}
}
Krüger, Timm; Kusumaatmaja, Halim; Kuzmin, Alexandr; Shardt, Orest; Silva, Goncalo; Viggen, Erlend Magnus
The lattice Boltzmann method: Principles and practice Book
Springer International Publishing, 2017, ISBN: 978-3-319-44647-9 / 978-3-319-44649-3.
BibTeX | Tags: lattice boltzmann | Links:
@book{kruger_lattice_2017,
title = {The lattice Boltzmann method: Principles and practice},
author = {Timm Krüger and Halim Kusumaatmaja and Alexandr Kuzmin and Orest Shardt and Goncalo Silva and Erlend Magnus Viggen},
url = {http://link.springer.com/10.1007/978-3-319-44649-3, Download through SpringerLink
http://www.springer.com/gp/book/9783319446479, Springer book page
https://github.com/lbm-principles-practice/errata/blob/master/errata.pdf, Book errata
},
doi = {10.1007/978-3-319-44649-3},
isbn = {978-3-319-44647-9 / 978-3-319-44649-3},
year = {2017},
date = {2017-01-01},
urldate = {2017-10-11},
publisher = {Springer International Publishing},
series = {Graduate Texts in Physics},
keywords = {lattice boltzmann},
pubstate = {published},
tppubtype = {book}
}
Gelderblom, Femke B; Tronstad, Tron V; Viggen, Erlend Magnus
Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement Proceedings Article
In: INTERSPEECH 2017, pp. 1968–1972, ISCA, 2017.
Abstract | BibTeX | Tags: machine learning, speech enhancement | Links:
@inproceedings{gelderblom_subjective_2017,
title = {Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement},
author = {Femke B Gelderblom and Tron V Tronstad and Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/319184981_Subjective_Intelligibility_of_Deep_Neural_Network-Based_Speech_Enhancement, Full-text on ResearchGate},
doi = {10.21437/Interspeech.2017-1041},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {INTERSPEECH 2017},
pages = {1968--1972},
publisher = {ISCA},
abstract = {Recent literature indicates increasing interest in deep neural networks for use in speech enhancement systems. Currently, these systems are mostly evaluated through objective measures of speech quality and/or intelligibility. Subjective intelligibility evaluations of these systems have so far not been reported. In this paper we report the results of a speech recognition test with 15 participants, where the participants were asked to pick out words in background noise before and after enhancement using a common deep neural network approach. We found that, although the objective measure STOI predicts that intelligibility should improve or at the very least stay the same, the speech recognition threshold, which is a measure of intelligibility, deteriorated by 4 dB. These results indicate that STOI is not a good predictor for the subjective intelligibility of deep neural network-based speech enhancement systems. We also found that the postprocessing technique of global variance normalisation does not significantly affect subjective intelligibility.},
keywords = {machine learning, speech enhancement},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent literature indicates increasing interest in deep neural networks for use in speech enhancement systems. Currently, these systems are mostly evaluated through objective measures of speech quality and/or intelligibility. Subjective intelligibility evaluations of these systems have so far not been reported. In this paper we report the results of a speech recognition test with 15 participants, where the participants were asked to pick out words in background noise before and after enhancement using a common deep neural network approach. We found that, although the objective measure STOI predicts that intelligibility should improve or at the very least stay the same, the speech recognition threshold, which is a measure of intelligibility, deteriorated by 4 dB. These results indicate that STOI is not a good predictor for the subjective intelligibility of deep neural network-based speech enhancement systems. We also found that the postprocessing technique of global variance normalisation does not significantly affect subjective intelligibility.
Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru
Simulation and inversion of ultrasonic pitch-catch through-tubing well logging with an array of receivers Journal Article
In: NDT & E International, vol. 85, pp. 72–75, 2017, ISSN: 09638695.
Abstract | BibTeX | Tags: acoustics, guided waves, well logging | Links:
@article{viggen_simulation_2017,
title = {Simulation and inversion of ultrasonic pitch-catch through-tubing well logging with an array of receivers},
author = {Erlend Magnus Viggen and Tonni Franke Johansen and Ioan-Alexandru Merciu},
url = {https://erlend-viggen.no/wp-content/uploads/2018/04/viggen_simulation_2017_post-print.pdf, Full-text},
doi = {10.1016/j.ndteint.2016.10.008},
issn = {09638695},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {NDT & E International},
volume = {85},
pages = {72--75},
abstract = {Current methods for ultrasonic pitch-catch well logging use two receivers to log the bonded material outside a single casing. For two casings separated by a fluid, we find by simulation that increasing the number of receivers provides a better picture of the effect of the bonded material outside the second casing. Inverting simulated measurements with five receivers, using a simulated annealing algorithm and a simple forward model, we find for a subset of simulations that we can estimate the impedance of the material outside the outer casing.},
keywords = {acoustics, guided waves, well logging},
pubstate = {published},
tppubtype = {article}
}
Current methods for ultrasonic pitch-catch well logging use two receivers to log the bonded material outside a single casing. For two casings separated by a fluid, we find by simulation that increasing the number of receivers provides a better picture of the effect of the bonded material outside the second casing. Inverting simulated measurements with five receivers, using a simulated annealing algorithm and a simple forward model, we find for a subset of simulations that we can estimate the impedance of the material outside the outer casing.
2016
Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru
Simulation and modeling of ultrasonic pitch-catch through-tubing logging Journal Article
In: Geophysics, vol. 81, no. 4, pp. D383-D393, 2016.
Abstract | BibTeX | Tags: acoustics, guided waves, well logging | Links:
@article{viggen_simulation_2016,
title = {Simulation and modeling of ultrasonic pitch-catch through-tubing logging},
author = {Erlend Magnus Viggen and Tonni Franke Johansen and Ioan-Alexandru Merciu},
url = {https://erlend-viggen.no/wp-content/uploads/2018/04/viggen_simulation_2016.pdf, Full-text},
doi = {10.1190/geo2015-0251.1},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Geophysics},
volume = {81},
number = {4},
pages = {D383-D393},
abstract = {Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the sealing material and to determine the structural integrity status. Although ultrasonic pitch-catch logging in single-casing geometries has been widely studied and is commercially available, this is not the case for logging in double-casing geometries despite its increasing importance in plug and abandonment operations. It is therefore important to investigate whether existing logging tools can be used in such geometries. Using a finite-element model of a double-casing geometry with a two-receiver pitch-catch setup, we have simulated through-tubing logging, with fluid between the two casings. We found that there appears a cascade of leaky Lamb wave packets on both casings, linked by leaked wavefronts. By varying the geometry and materials in the model, we have examined the effect on the pulse received from the second wave packet on the inner casing, sometimes known as the third interface echo. The amplitude of this pulse was found to contain information on the bonded material in the outer annulus. Much stronger amplitude variations were found with two equally thick casings than with a significant thickness difference; relative thickness differences of up to one-third were simulated. Finally, we have developed a simple mathematical model of the wave packets’ time evolution to encapsulate and validate our understanding of the wave packet cascade. This model shows a more complex time evolution in the later wave packets than the exponentially attenuated primary packet, which is currently used for single-casing logging. This indicates that tools with more than two receivers, which could measure wave packets’ amplitude at more than two points along their time evolution, would be able to draw more information from these later packets. The model was validated against simulations, finding good agreement when the underlying assumptions of the model were satisfied.},
keywords = {acoustics, guided waves, well logging},
pubstate = {published},
tppubtype = {article}
}
Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the sealing material and to determine the structural integrity status. Although ultrasonic pitch-catch logging in single-casing geometries has been widely studied and is commercially available, this is not the case for logging in double-casing geometries despite its increasing importance in plug and abandonment operations. It is therefore important to investigate whether existing logging tools can be used in such geometries. Using a finite-element model of a double-casing geometry with a two-receiver pitch-catch setup, we have simulated through-tubing logging, with fluid between the two casings. We found that there appears a cascade of leaky Lamb wave packets on both casings, linked by leaked wavefronts. By varying the geometry and materials in the model, we have examined the effect on the pulse received from the second wave packet on the inner casing, sometimes known as the third interface echo. The amplitude of this pulse was found to contain information on the bonded material in the outer annulus. Much stronger amplitude variations were found with two equally thick casings than with a significant thickness difference; relative thickness differences of up to one-third were simulated. Finally, we have developed a simple mathematical model of the wave packets’ time evolution to encapsulate and validate our understanding of the wave packet cascade. This model shows a more complex time evolution in the later wave packets than the exponentially attenuated primary packet, which is currently used for single-casing logging. This indicates that tools with more than two receivers, which could measure wave packets’ amplitude at more than two points along their time evolution, would be able to draw more information from these later packets. The model was validated against simulations, finding good agreement when the underlying assumptions of the model were satisfied.
Viggen, Erlend Magnus; Kristiansen, Ulf R (Ed.)
Proceedings of the 39th Scandinavian Symposium on Physical Acoustics Book
2016, ISBN: 978-82-8123-016-3.
BibTeX | Tags: acoustics | Links:
@book{viggen_proceedings_2016,
title = {Proceedings of the 39th Scandinavian Symposium on Physical Acoustics},
editor = {Erlend Magnus Viggen and Ulf R Kristiansen},
url = {https://arxiv.org/html/1604.01763, Link to proceedings},
isbn = {978-82-8123-016-3},
year = {2016},
date = {2016-04-11},
keywords = {acoustics},
pubstate = {published},
tppubtype = {book}
}
Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru
Analysis of outer-casing echoes in simulations of ultrasonic pulse-echo through-tubing logging Journal Article
In: Geophysics, vol. 81, no. 6, pp. D679–D685, 2016, ISSN: 0016-8033, 1942-2156.
Abstract | BibTeX | Tags: acoustics, well logging | Links:
@article{viggen_analysis_2016,
title = {Analysis of outer-casing echoes in simulations of ultrasonic pulse-echo through-tubing logging},
author = {Erlend Magnus Viggen and Tonni Franke Johansen and Ioan-Alexandru Merciu},
url = {https://erlend-viggen.no/wp-content/uploads/2018/04/viggen_analysis_2016.pdf, Full-text},
doi = {10.1190/geo2015-0376.1},
issn = {0016-8033, 1942-2156},
year = {2016},
date = {2016-01-01},
urldate = {2017-10-11},
journal = {Geophysics},
volume = {81},
number = {6},
pages = {D679--D685},
abstract = {Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the cement. Ultrasonic logging of single casings has been widely studied and is commercially available. However, ultrasonic logging in multiple-casing geometries is an unexplored topic despite its importance in plug and abandonment operations. Therefore, current logging technologies should be studied to evaluate whether they indicate the potential for multiple-casing logging. In this study, we used two finite-element models of pulse-echo logging. The first model represents logging in the transverse cross section of a double-casing well. The second model is a copy of the first, but with the outer casing and formation removed so that the pulse-echo transducer receives only a resonant first interface echo from the inner casing. By subtracting the received signals of the second model from those of the first, we can recover the third interface echo (TIE) signal representing the resonant reflection from the outer casing. This signal is used to study what information can, in principle, be drawn from TIEs in double-casing geometries, with the caveat that TIEs can only approximately be recovered in practical cases. We simulated variations of the material in the annulus beyond the outer casing, of the thickness of the outer casing, and of the eccentering of the outer casing. We have determined that the first two of these variations have only weak effects on the TIE, but that the eccentering of the outer casing can, in principle, be found using the TIE arrival time.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {article}
}
Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the cement. Ultrasonic logging of single casings has been widely studied and is commercially available. However, ultrasonic logging in multiple-casing geometries is an unexplored topic despite its importance in plug and abandonment operations. Therefore, current logging technologies should be studied to evaluate whether they indicate the potential for multiple-casing logging. In this study, we used two finite-element models of pulse-echo logging. The first model represents logging in the transverse cross section of a double-casing well. The second model is a copy of the first, but with the outer casing and formation removed so that the pulse-echo transducer receives only a resonant first interface echo from the inner casing. By subtracting the received signals of the second model from those of the first, we can recover the third interface echo (TIE) signal representing the resonant reflection from the outer casing. This signal is used to study what information can, in principle, be drawn from TIEs in double-casing geometries, with the caveat that TIEs can only approximately be recovered in practical cases. We simulated variations of the material in the annulus beyond the outer casing, of the thickness of the outer casing, and of the eccentering of the outer casing. We have determined that the first two of these variations have only weak effects on the TIE, but that the eccentering of the outer casing can, in principle, be found using the TIE arrival time.
2015
Viggen, Erlend Magnus; Solvang, Audun; Vennerød, Jakob; Olsen, Herold
Development of an outdoor auralisation prototype with 3D sound reproduction Proceedings Article
In: Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), Trondheim, 2015.
Abstract | BibTeX | Tags: acoustics, auralisation | Links:
@inproceedings{viggen_development_2015,
title = {Development of an outdoor auralisation prototype with 3D sound reproduction},
author = {Erlend Magnus Viggen and Audun Solvang and Jakob Vennerød and Herold Olsen},
url = {https://www.researchgate.net/publication/285592626_Development_of_an_outdoor_auralisation_prototype_with_3D_sound_reproduction, Full-text on ResearchGate},
year = {2015},
date = {2015-12-01},
urldate = {2015-12-01},
booktitle = {Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15)},
address = {Trondheim},
abstract = {Auralisation of outdoor sound has a strong potential for demonstrating the impact of different community noise scenarios. We describe here the development of an auralisation tool for outdoor noise such as traffic or industry. The tool calculates the sound propagation from source to listener using the Nord2000 model, and represents the sound field at the listener's position using spherical harmonics. Because of this spherical harmonics approach, the sound may be reproduced in various formats, such as headphones, stereo, or surround. Dynamic reproduction in headphones according to the listener's head orientation is also possible through the use of head tracking.},
keywords = {acoustics, auralisation},
pubstate = {published},
tppubtype = {inproceedings}
}
Auralisation of outdoor sound has a strong potential for demonstrating the impact of different community noise scenarios. We describe here the development of an auralisation tool for outdoor noise such as traffic or industry. The tool calculates the sound propagation from source to listener using the Nord2000 model, and represents the sound field at the listener's position using spherical harmonics. Because of this spherical harmonics approach, the sound may be reproduced in various formats, such as headphones, stereo, or surround. Dynamic reproduction in headphones according to the listener's head orientation is also possible through the use of head tracking.
2014
Viggen, Erlend Magnus
The lattice Boltzmann method: Fundamentals and acoustics PhD Thesis
Norwegian University of Science and Technology, 2014.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@phdthesis{viggen_lattice_2014,
title = {The lattice Boltzmann method: Fundamentals and acoustics},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/263714289_The_lattice_Boltzmann_method_Fundamentals_and_acoustics, Full-text on ResearchGate},
year = {2014},
date = {2014-02-21},
address = {Trondheim},
school = {Norwegian University of Science and Technology},
abstract = {The lattice Boltzmann method has been widely used as a solver for incompressible flow, though it is not restricted to this application. More generally, it can be used as a compressible Navier-Stokes solver, albeit with a restriction that the Mach number is low. While that restriction may seem strict, it does not hinder the application of the method to the simulation of sound waves, for which the Mach numbers are generally very low. Even sound waves with strong nonlinear effects can be captured well. Despite this, the method has not been as widely used for problems where acoustic phenomena are involved as it has been for incompressible problems.
The research presented this thesis goes into three different aspects of lattice Boltzmann acoustics. Firstly, linearisation analyses are used to derive and compare the sound propagation properties of the lattice Boltzmann equation and comparable fluid models for both free and forced waves. The propagation properties of the fully discrete lattice Boltzmann equation are shown to converge at second order towards those of the discrete-velocity Boltzmann equation, which itself predicts the same lowest-order absorption but different dispersion to the other fluid models.
Secondly, it is shown how multipole sound sources can be created mesoscopically by adding a particle source term to the Boltzmann equation. This method is straightforwardly extended to the lattice Boltzmann method by discretisation. The results of lattice Boltzmann simulations of monopole, dipole, and quadrupole point sources are shown to agree very well with the combined predictions of this multipole method and the linearisation analysis. The exception to this agreement is the immediate vicinity of the point source, where the singularity in the analytical solution cannot be reproduced numerically.
Thirdly, an extended lattice Boltzmann model is described. This model alters the equilibrium distribution to reproduce variable equations of state while remaining simple to implement and efficient to run. To compensate for an unphysical bulk viscosity, the extended model contains a bulk viscosity correction term. It is shown that all equilibrium distributions that allow variable equations of state must be identical for the one-dimensional D1Q3 velocity set. Using such a D1Q3 velocity set and an isentropic equation of state, both mechanisms of nonlinear acoustics are captured successfully in a simulation, improving on previous isothermal simulations where only one mechanism could be captured. In addition, the effect of molecular relaxation on sound propagation is simulated using a model equation of state. Though the particular implementation used is not completely stable, the results agree well with theory.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {phdthesis}
}
The lattice Boltzmann method has been widely used as a solver for incompressible flow, though it is not restricted to this application. More generally, it can be used as a compressible Navier-Stokes solver, albeit with a restriction that the Mach number is low. While that restriction may seem strict, it does not hinder the application of the method to the simulation of sound waves, for which the Mach numbers are generally very low. Even sound waves with strong nonlinear effects can be captured well. Despite this, the method has not been as widely used for problems where acoustic phenomena are involved as it has been for incompressible problems.
The research presented this thesis goes into three different aspects of lattice Boltzmann acoustics. Firstly, linearisation analyses are used to derive and compare the sound propagation properties of the lattice Boltzmann equation and comparable fluid models for both free and forced waves. The propagation properties of the fully discrete lattice Boltzmann equation are shown to converge at second order towards those of the discrete-velocity Boltzmann equation, which itself predicts the same lowest-order absorption but different dispersion to the other fluid models.
Secondly, it is shown how multipole sound sources can be created mesoscopically by adding a particle source term to the Boltzmann equation. This method is straightforwardly extended to the lattice Boltzmann method by discretisation. The results of lattice Boltzmann simulations of monopole, dipole, and quadrupole point sources are shown to agree very well with the combined predictions of this multipole method and the linearisation analysis. The exception to this agreement is the immediate vicinity of the point source, where the singularity in the analytical solution cannot be reproduced numerically.
Thirdly, an extended lattice Boltzmann model is described. This model alters the equilibrium distribution to reproduce variable equations of state while remaining simple to implement and efficient to run. To compensate for an unphysical bulk viscosity, the extended model contains a bulk viscosity correction term. It is shown that all equilibrium distributions that allow variable equations of state must be identical for the one-dimensional D1Q3 velocity set. Using such a D1Q3 velocity set and an isentropic equation of state, both mechanisms of nonlinear acoustics are captured successfully in a simulation, improving on previous isothermal simulations where only one mechanism could be captured. In addition, the effect of molecular relaxation on sound propagation is simulated using a model equation of state. Though the particular implementation used is not completely stable, the results agree well with theory.
The research presented this thesis goes into three different aspects of lattice Boltzmann acoustics. Firstly, linearisation analyses are used to derive and compare the sound propagation properties of the lattice Boltzmann equation and comparable fluid models for both free and forced waves. The propagation properties of the fully discrete lattice Boltzmann equation are shown to converge at second order towards those of the discrete-velocity Boltzmann equation, which itself predicts the same lowest-order absorption but different dispersion to the other fluid models.
Secondly, it is shown how multipole sound sources can be created mesoscopically by adding a particle source term to the Boltzmann equation. This method is straightforwardly extended to the lattice Boltzmann method by discretisation. The results of lattice Boltzmann simulations of monopole, dipole, and quadrupole point sources are shown to agree very well with the combined predictions of this multipole method and the linearisation analysis. The exception to this agreement is the immediate vicinity of the point source, where the singularity in the analytical solution cannot be reproduced numerically.
Thirdly, an extended lattice Boltzmann model is described. This model alters the equilibrium distribution to reproduce variable equations of state while remaining simple to implement and efficient to run. To compensate for an unphysical bulk viscosity, the extended model contains a bulk viscosity correction term. It is shown that all equilibrium distributions that allow variable equations of state must be identical for the one-dimensional D1Q3 velocity set. Using such a D1Q3 velocity set and an isentropic equation of state, both mechanisms of nonlinear acoustics are captured successfully in a simulation, improving on previous isothermal simulations where only one mechanism could be captured. In addition, the effect of molecular relaxation on sound propagation is simulated using a model equation of state. Though the particular implementation used is not completely stable, the results agree well with theory.
Viggen, Erlend Magnus
Acoustic equations of state for simple lattice Boltzmann velocity sets Journal Article
In: Physical Review E, vol. 90, pp. 013310, 2014, ISSN: 1539-3755, 1550-2376.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@article{viggen_acoustic_2014,
title = {Acoustic equations of state for simple lattice Boltzmann velocity sets},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/264397832_Acoustic_equations_of_state_for_simple_lattice_Boltzmann_velocity_sets, Full-text on ResearchGate},
doi = {10.1103/PhysRevE.90.013310},
issn = {1539-3755, 1550-2376},
year = {2014},
date = {2014-01-01},
urldate = {2018-04-04},
journal = {Physical Review E},
volume = {90},
pages = {013310},
abstract = {The lattice Boltzmann (LB) method typically uses an isothermal equation of state. This is not sufficient to simulate a number of acoustic phenomena where the equation of state cannot be approximated as linear and constant. However, it is possible to implement variable equations of state by altering the LB equilibrium distribution. For simple velocity sets with velocity components ξiα ∈ −1,0,1 for all i, these equilibria necessarily cause error terms in the momentum equation. These error terms are shown to be either correctable or negligible at the cost of further weakening the compressibility. For the D1Q3 velocity set, such an equilibrium distribution is found and shown to be unique. Its sound propagation properties are found for both forced and free waves, with some generality beyond D1Q3. Finally, this equilibrium distribution is applied to a nonlinear acoustics simulation where both mechanisms of nonlinearity are simulated with good results. This represents an improvement on previous such simulations and proves that the compressibility of the method is still sufficiently strong even for nonlinear acoustics.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {article}
}
The lattice Boltzmann (LB) method typically uses an isothermal equation of state. This is not sufficient to simulate a number of acoustic phenomena where the equation of state cannot be approximated as linear and constant. However, it is possible to implement variable equations of state by altering the LB equilibrium distribution. For simple velocity sets with velocity components ξiα ∈ −1,0,1 for all i, these equilibria necessarily cause error terms in the momentum equation. These error terms are shown to be either correctable or negligible at the cost of further weakening the compressibility. For the D1Q3 velocity set, such an equilibrium distribution is found and shown to be unique. Its sound propagation properties are found for both forced and free waves, with some generality beyond D1Q3. Finally, this equilibrium distribution is applied to a nonlinear acoustics simulation where both mechanisms of nonlinearity are simulated with good results. This represents an improvement on previous such simulations and proves that the compressibility of the method is still sufficiently strong even for nonlinear acoustics.
2013
Viggen, Erlend Magnus
Sound propagation properties of the discrete-velocity Boltzmann equation Journal Article
In: Communications in Computational Physics, vol. 13, no. 3, pp. 671–684, 2013.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@article{viggen_sound_2013,
title = {Sound propagation properties of the discrete-velocity Boltzmann equation},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/263714278_Sound_Propagation_Properties_of_the_Discrete-Velocity_Boltzmann_Equation, Full-text on ResearchGate},
doi = {10.4208/cicp.271011.020212s},
year = {2013},
date = {2013-03-01},
journal = {Communications in Computational Physics},
volume = {13},
number = {3},
pages = {671--684},
abstract = {As the numerical resolution is increased and the discretisation error decreases, the lattice Boltzmann method tends towards the discrete-velocity Boltzmann equation (DVBE). An expression for the propagation properties of plane sound waves is found for this equation. This expression is compared to similar ones from the Navier-Stokes and Burnett models, and is found to be closest to the latter. The anisotropy of sound propagation with the DVBE is examined using a two-dimensional velocity set. It is found that both the anisotropy and the deviation between the models is negligible if the Knudsen number is less than 1 by at least an order of magnitude.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {article}
}
As the numerical resolution is increased and the discretisation error decreases, the lattice Boltzmann method tends towards the discrete-velocity Boltzmann equation (DVBE). An expression for the propagation properties of plane sound waves is found for this equation. This expression is compared to similar ones from the Navier-Stokes and Burnett models, and is found to be closest to the latter. The anisotropy of sound propagation with the DVBE is examined using a two-dimensional velocity set. It is found that both the anisotropy and the deviation between the models is negligible if the Knudsen number is less than 1 by at least an order of magnitude.
Viggen, Erlend Magnus
Acoustic multipole sources from the Boltzmann equation Proceedings Article
In: Proceedings of the 36th Scandinavian Symposium on Physical Acoustics, Norwegian Physical Society, Geilo, Norway, 2013.
Abstract | BibTeX | Tags: acoustics | Links:
@inproceedings{viggen_acoustic_2013b,
title = {Acoustic multipole sources from the Boltzmann equation},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/235633945_Acoustic_multipole_sources_from_the_Boltzmann_equation, Full-text on ResearchGate},
year = {2013},
date = {2013-02-01},
urldate = {2013-02-01},
booktitle = {Proceedings of the 36th Scandinavian Symposium on Physical Acoustics},
publisher = {Norwegian Physical Society},
address = {Geilo, Norway},
abstract = {By adding a particle source term in the Boltzmann equation of kinetic theory, it is possible to represent particles appearing and disappearing throughout the fluid with a specified distribution of particle velocities. By deriving the wave equation from this modified Boltzmann equation via the conservation equations of fluid mechanics, multipole source terms in the wave equation are found. These multipole source terms are given by the particle source term in the Boltzmann equation. To the Euler level in the momentum equation, a monopole and a dipole source term appear in the wave equation. To the Navier-Stokes level, a quadrupole term with negligible magnitude also appears.},
keywords = {acoustics},
pubstate = {published},
tppubtype = {inproceedings}
}
By adding a particle source term in the Boltzmann equation of kinetic theory, it is possible to represent particles appearing and disappearing throughout the fluid with a specified distribution of particle velocities. By deriving the wave equation from this modified Boltzmann equation via the conservation equations of fluid mechanics, multipole source terms in the wave equation are found. These multipole source terms are given by the particle source term in the Boltzmann equation. To the Euler level in the momentum equation, a monopole and a dipole source term appear in the wave equation. To the Navier-Stokes level, a quadrupole term with negligible magnitude also appears.
Viggen, Erlend Magnus
Acoustic multipole sources for the lattice Boltzmann method Journal Article
In: Physical Review E, vol. 87, no. 2, pp. 023306, 2013.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@article{viggen_acoustic_2013,
title = {Acoustic multipole sources for the lattice Boltzmann method},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/236051059_Acoustic_multipole_sources_for_the_lattice_Boltzmann_method, Full-text on ResearchGate},
doi = {10.1103/PhysRevE.87.023306},
year = {2013},
date = {2013-01-01},
journal = {Physical Review E},
volume = {87},
number = {2},
pages = {023306},
abstract = {By including an oscillating particle source term, acoustic multipole sources can be implemented in the lattice Boltzmann method. The effect of this source term on the macroscopic conservation equations is found using a Chapman-Enskog expansion. In a lattice with q particle velocities, the source term can be decomposed into q orthogonal multipoles. More complex sources may be formed by superposing these basic multipoles. Analytical solutions found from the macroscopic equations and an analytical lattice Boltzmann wavenumber are compared with inviscid multipole simulations, finding very good agreement except close to singularities in the analytical solutions. Unlike the BGK operator, the regularized collision operator is proven capable of accurately simulating two-dimensional acoustic generation and propagation at zero viscosity.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {article}
}
By including an oscillating particle source term, acoustic multipole sources can be implemented in the lattice Boltzmann method. The effect of this source term on the macroscopic conservation equations is found using a Chapman-Enskog expansion. In a lattice with q particle velocities, the source term can be decomposed into q orthogonal multipoles. More complex sources may be formed by superposing these basic multipoles. Analytical solutions found from the macroscopic equations and an analytical lattice Boltzmann wavenumber are compared with inviscid multipole simulations, finding very good agreement except close to singularities in the analytical solutions. Unlike the BGK operator, the regularized collision operator is proven capable of accurately simulating two-dimensional acoustic generation and propagation at zero viscosity.
2011
Viggen, Erlend Magnus
Viscously damped acoustic waves with the lattice Boltzmann method Journal Article
In: Philosophical Transactions of the Royal Society A, vol. 369, no. 1944, pp. 2246–2254, 2011.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@article{viggen_viscously_2011,
title = {Viscously damped acoustic waves with the lattice Boltzmann method},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/51092609_Viscously_damped_acoustic_waves_with_the_lattice_Boltzmann_method, Full-text on ResearchGate},
doi = {10.1098/rsta.2011.0040},
year = {2011},
date = {2011-06-01},
journal = {Philosophical Transactions of the Royal Society A},
volume = {369},
number = {1944},
pages = {2246--2254},
abstract = {Acoustic wave propagation in lattice Boltzmann Bhatnagar-Gross-Krook simulations may be analysed using a linearization method. This method has been used in the past to study the propagation of waves that are viscously damped in time, and is here extended to also study waves that are viscously damped in space. Its validity is verified against simulations, and the results are compared with theoretical expressions. It is found in the infinite resolution limit k→0 that the absorption coefficients and phase differences between density and velocity waves match theoretical expressions for small values of ωτ(ν), the characteristic number for viscous acoustic damping. However, the phase velocities and amplitude ratios between the waves increase incorrectly with (ωτ(ν))(2), and agree with theory only in the inviscid limit k→0, ωτ(ν)→0. The actual behaviour of simulated plane waves in the infinite resolution limit is quantified.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {article}
}
Acoustic wave propagation in lattice Boltzmann Bhatnagar-Gross-Krook simulations may be analysed using a linearization method. This method has been used in the past to study the propagation of waves that are viscously damped in time, and is here extended to also study waves that are viscously damped in space. Its validity is verified against simulations, and the results are compared with theoretical expressions. It is found in the infinite resolution limit k→0 that the absorption coefficients and phase differences between density and velocity waves match theoretical expressions for small values of ωτ(ν), the characteristic number for viscous acoustic damping. However, the phase velocities and amplitude ratios between the waves increase incorrectly with (ωτ(ν))(2), and agree with theory only in the inviscid limit k→0, ωτ(ν)→0. The actual behaviour of simulated plane waves in the infinite resolution limit is quantified.
2010
Viggen, Erlend Magnus
The lattice Boltzmann method in acoustics Proceedings Article
In: Proceedings of the 33rd Scandinavian Symposium on Physical Acoustics, Norwegian Physical Society, Geilo, Norway, 2010.
Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:
@inproceedings{viggen_lattice_2010,
title = {The lattice Boltzmann method in acoustics},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/263739190_The_lattice_Boltzmann_method_in_acoustics, Full-text on ResearchGate},
year = {2010},
date = {2010-02-01},
urldate = {2010-02-01},
booktitle = {Proceedings of the 33rd Scandinavian Symposium on Physical Acoustics},
publisher = {Norwegian Physical Society},
address = {Geilo, Norway},
abstract = {The lattice Boltzmann method, a method based in kinetic theory and used for simulating fluid behaviour, is presented with particular regard to usage in acoustics. A point source method of generating acoustic waves in the computational domain is presented, and simple simulation results with this method are analysed. The simulated waves' transient wavefronts in one dimension are shown to agree with analytical solutions from acoustic theory. The phase velocity and absorption coefficients of the waves and their deviations from theory are analysed. Finally, the physical time and space steps relating simulation units with physical units are discussed and shown to limit acoustic usage of the method to small scales in time and space.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {inproceedings}
}
The lattice Boltzmann method, a method based in kinetic theory and used for simulating fluid behaviour, is presented with particular regard to usage in acoustics. A point source method of generating acoustic waves in the computational domain is presented, and simple simulation results with this method are analysed. The simulated waves' transient wavefronts in one dimension are shown to agree with analytical solutions from acoustic theory. The phase velocity and absorption coefficients of the waves and their deviations from theory are analysed. Finally, the physical time and space steps relating simulation units with physical units are discussed and shown to limit acoustic usage of the method to small scales in time and space.
2009
Viggen, Erlend Magnus
The Lattice Boltzmann Method with Applications in Acoustics Masters Thesis
Norwegian University of Science and Technology (NTNU), Trondheim, 2009.
BibTeX | Tags: acoustics, lattice boltzmann | Links:
@mastersthesis{viggen_lattice_2009,
title = {The Lattice Boltzmann Method with Applications in Acoustics},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/242162544_The_Lattice_Boltzmann_Method_with_Applications_in_Acoustics, Full-text on ResearchGate},
year = {2009},
date = {2009-07-01},
address = {Trondheim},
school = {Norwegian University of Science and Technology (NTNU)},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {mastersthesis}
}
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