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Proceedings Papers

Advancement in Seismic Data Conditioning Using Tailored Deep Neural Networks

Mujeeb Oke, Hesham Hasan, Hiren Maniar, Georges Assaf, Tao Zhao, Aria Abubakar, Houcine Ben Jeddou, Chakib Kada Kloucha, Hussein Mustapha
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222977-MS
https://doi.org/10.2118/222977-MS
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View Paper titled, Advancement in Seismic Data Conditioning Using Tailored Deep Neural Networks
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Proceedings Papers

Introducing stochasticity into CNN-based property estimation from angle-stack seismic

Haibin Di, Tao Zhao, Aria Abubakar
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4076489
https://doi.org/10.1190/image2024-4076489.1
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View Paper titled, Introducing stochasticity into CNN-based property estimation from angle-stack seismic
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Proceedings Papers

Automating the thresholding of multi-stage iterative source separation with priors using machine learning

Nam Pham, Rajiv Kumar, Sunil Manikani, Yousif Izzeldin Kamil Amin, Phillip Bilsby, Massimiliano Vassallo, Tao Zhao
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4100817
https://doi.org/10.1190/image2024-4100817.1
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View Paper titled, Automating the thresholding of multi-stage iterative source separation with priors using machine learning
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Proceedings Papers

A practical approach to automate end-to-end multi-stage iterative source separation with prior framework using machine learning

Rajiv Kumar, Yousif Izzeldin Kamil Amin, Riccardo Giro, Sunil Manikani, Nam Pham, Massimiliano Vassallo, Phillip Bilsby, Tao Zhao
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4099335
https://doi.org/10.1190/image2024-4099335.1
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View Paper titled, A practical approach to automate end-to-end multi-stage iterative source separation with prior framework using machine learning
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Proceedings Papers

A Deep Learning Workflow for Integrated Geological, Petrophysical, and Geomechanical Interpretation

Vanessa Simoes, Atul Katole, Bhuvaneswari Sankaranarayanan, Tao Zhao, Aria Abubakar
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4040968-MS
https://doi.org/10.15530/urtec-2024-4040968
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View Paper titled, A Deep Learning Workflow for Integrated Geological, Petrophysical, and Geomechanical Interpretation
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Proceedings Papers

Machine Learning Based Automatic Marker Clustering

Atul Laxman Katole, Aria Abubakar, Edo Hoekstra, Srikanth Ryali, Tao Zhao
Paper presented at the SPE Annual Technical Conference and Exhibition, October 16–18, 2023
Paper Number: SPE-215065-MS
https://doi.org/10.2118/215065-MS
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View Paper titled, Machine Learning Based Automatic Marker Clustering
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Proceedings Papers

Deep learning based automatic marker separation

Atul Laxman Katole, Aria Abubakar, Edo Hoekstra, Srikanth Ryali, Tao Zhao
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3916091
https://doi.org/10.1190/image2023-3916091.1
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View Paper titled, Deep learning based automatic marker separation
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Proceedings Papers

Cascaded deep learning for fast-track structure model building from 2D seismic data

Tao Zhao, Leigh Truelove
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3906927
https://doi.org/10.1190/image2023-3906927.1
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View Paper titled, Cascaded deep learning for fast-track structure model building from 2D seismic data
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Journal Articles

Comparative Study of Machine-Learning-Based Methods for Log Prediction

Vanessa Simoes, Hiren Maniar, Aria Abubakar, Tao Zhao
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 64 (02): 192–212.
Paper Number: SPWLA-2023-v64n2a4
https://doi.org/10.30632/PJV64N2-2023a4
Published: 04 April 2023
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Journal Articles

Deep Learning for Multiwell Automatic Log Correction

Vanessa Simoes, Hiren Maniar, Aria Abubakar, Tao Zhao
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 63 (06): 724–747.
Paper Number: SPWLA-2022-v63n6a10
https://doi.org/10.30632/PJV63N6-2022a10
Published: 12 December 2022
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Proceedings Papers

Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction

Tao Zhao, Haibin Di, Aria Abubakar
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3746933
https://doi.org/10.1190/image2022-3746933.1
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View Paper titled, Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction
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Proceedings Papers

Deep Learning for Multiwell Automatic Log Correction

Vanessa Simoes, Hiren Maniar, Aria Abubakar, Tao Zhao
Paper presented at the SPWLA 63rd Annual Logging Symposium, June 11–15, 2022
Paper Number: SPWLA-2022-0070
https://doi.org/10.30632/SPWLA-2022-0070
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Proceedings Papers

A Comparative Study for Machine-Learning-Based Methods for Log Prediction

Vanessa Simoes, Hiren Maniar, Tao Zhao, Aria Abubakar, Ridvan Akkurt
Paper presented at the SPWLA 63rd Annual Logging Symposium, June 11–15, 2022
Paper Number: SPWLA-2022-0067
https://doi.org/10.30632/SPWLA-2022-0067
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View Paper titled, A Comparative Study for Machine-Learning-Based Methods for Log Prediction
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Proceedings Papers

Automatic salt geometry update using deep learning in iterative FWI-RTM workflows

Tao Zhao, Chunpeng Zhao, Anisha Kaul, Aria Abubakar
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582052
https://doi.org/10.1190/segam2021-3582052.1
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View Paper titled, Automatic salt geometry update using deep learning in iterative FWI-RTM workflows
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Proceedings Papers

Validating the goodness-of-fit of machine learning models through backward models

Tao Zhao, Bhuvaneswari Sankaranarayanan, Haibin Di, Aria Abubakar
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582062
https://doi.org/10.1190/segam2021-3582062.1
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View Paper titled, Validating the goodness-of-fit of machine learning models through backward models
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Proceedings Papers

Deep learning applications for wind farms site characterization and monitoring

Aria Abubakar, Mette Juncker Brædstrup, Haibin Di, Alberto Troya Diaz, Steve Freeman, Simon Hviid, Knud Ho Karkov, Sachin Kriplani, Sunil Manikani, Gwenaëlle Salun, Tao Zhao
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583026
https://doi.org/10.1190/segam2021-3583026.1
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View Paper titled, Deep learning applications for wind farms site characterization and monitoring
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Proceedings Papers

Slope Stability Analysis via Discrete Element Method and Monte Carlo Simulations

Fei Xie, Chun Liu, Tao Zhao, Guo-Qing Xia
Paper presented at the ISRM Regional Symposium - 11th Asian Rock Mechanics Symposium, October 21–25, 2021
Paper Number: ISRM-ARMS11-2021-484
https://doi.org/10.1088/1755-1315/861/3/032023
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View Paper titled, Slope Stability Analysis via Discrete Element Method and Monte Carlo Simulations
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Proceedings Papers

Enrich the interpretation of seismic image segmentation by estimating epistemic uncertainty

Tao Zhao, Xiaoli Chen
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424987
https://doi.org/10.1190/segam2020-3424987.1
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View Paper titled, Enrich the interpretation of seismic image segmentation by estimating epistemic uncertainty
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Proceedings Papers

Augment time-domain FWI with iterative deep learning

Tao Zhao, Aria Abubakar, Xin Cheng, Lei Fu
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424983
https://doi.org/10.1190/segam2020-3424983.1
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View Paper titled, Augment time-domain FWI with iterative deep learning
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Proceedings Papers

3D convolutional neural networks for efficient fault detection and orientation estimation

Tao Zhao
Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3216307
https://doi.org/10.1190/segam2019-3216307.1
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View Paper titled, 3D convolutional neural networks for efficient fault detection and orientation estimation
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