Publications - Erlend M. Viggen

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.

acoustics auralisation guided waves lattice boltzmann machine learning speech enhancement well logging

2024

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. 1, 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 = {2022-12-19},

journal = {Mechanical Systems and Signal Processing},

volume = {1},

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}

}

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}

}

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:

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:

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:

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}

}

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:

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:

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:

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:

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}

}

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:

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:

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:

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:

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:

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.

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:

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:

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:

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}

}

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.

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:

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}

}

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:

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}

}

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:

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:

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:

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:

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}

}

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:

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}

}

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:

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.

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: