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Journal Articles

Automatic Depth Shifting by Identifying and Matching Events on Well Logs

Chicheng Xu, Lei Fu, Tao Lin, Weichang Li, Yaser Alzayer, Zainab Al Ibrahim
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 65 (02): 246–255.
Paper Number: SPWLA-2024-v65n2a07
https://doi.org/10.30632/PJV65N2-2024a7
Published: 15 April 2024
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Proceedings Papers

Automated Wellhead Monitoring Using Deep Learning from Multimodal Imaging

Weichang Li, Yong Ma, Damian San Roman Alerigi
Paper presented at the International Petroleum Technology Conference, February 12, 2024
Paper Number: IPTC-23632-MS
https://doi.org/10.2523/IPTC-23632-MS
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View Paper titled, Automated Wellhead Monitoring Using Deep Learning from Multimodal Imaging
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Proceedings Papers

Machine Learning Assisted Petrophysical and Geochemical Reservoir Description Integrating Multi-Scale Well Data

Chicheng Xu, Yuchen Jin, Tao Lin, Weichang Li, Yaser Alzayer, Zainab Ibrahim
Paper presented at the SPE Annual Technical Conference and Exhibition, October 16–18, 2023
Paper Number: SPE-214963-MS
https://doi.org/10.2118/214963-MS
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View Paper titled, Machine Learning Assisted Petrophysical and Geochemical Reservoir Description Integrating Multi-Scale Well Data
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Journal Articles

Python Dash for Well Data Validation, Visualization, and Processing

Yuchen Jin, Chicheng Xu, Tao Lin, Weichang Li, Mohamed Larbi Zeghlache
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 64 (04): 568–573.
Paper Number: SPWLA-2023-v64n4a6
https://doi.org/10.30632/PJV64N4-2023a6
Published: 01 August 2023
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View article titled, Python Dash for Well Data Validation, Visualization, and Processing
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Proceedings Papers

Well Log Prediction Using Deep Sequence Learning

Weichang Li, Lei Fu, Murtadha J. AlTammar
Paper presented at the International Geomechanics Symposium, November 7–10, 2022
Paper Number: ARMA-IGS-2022-184
https://doi.org/10.56952/IGS-2022-184
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View Paper titled, Well Log Prediction Using Deep Sequence Learning
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Proceedings Papers

Deep learning Laplace-Fourier full-waveform inversion with virtual supershot gathers

Lei Fu, Daniele Colombo, Weichang Li, Ernesto Sandoval-Curiel, Ersan Turkoglu
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745792
https://doi.org/10.1190/image2022-3745792.1
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Proceedings Papers

Deep learning seismic full-waveform inversion and transient EM joint inversion for near surface velocity modeling

Daniele Colombo, Ernesto Sandoval-Curiel, Ersan Turkoglu, Weichang Li
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3746223
https://doi.org/10.1190/image2022-3746223.1
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View Paper titled, Deep learning seismic full-waveform inversion and transient EM joint inversion for near surface velocity modeling
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Proceedings Papers

Comparing DAS and geophone data and imaging for monitoring CO 2 sequestration

Yong Ma, Weichang Li
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3734299
https://doi.org/10.1190/image2022-3734299.1
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View Paper titled, Comparing DAS and geophone data and imaging for monitoring CO 2 sequestration
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Proceedings Papers

Deep Learning Based Production Flow Profiling and Allocation Using Fiber Optic DAS and DTS Data

Weichang Li, Frode Hveding
Paper presented at the 23rd World Petroleum Congress, December 5–9, 2021
Paper Number: WPC-23-1401
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View Paper titled, Deep Learning Based Production Flow Profiling and Allocation Using Fiber Optic DAS and DTS Data
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Proceedings Papers

Predicting density and velocity ahead of the bit with zero-offset VSP using deep learning

Vladimir Kazei, Aleksei Titov, Weichang Li, Konstantin Osypov
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594957
https://doi.org/10.1190/segam2021-3594957.1
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View Paper titled, Predicting density and velocity ahead of the bit with zero-offset VSP using deep learning
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Proceedings Papers

A framework for coupled physics-deep learning inversion and multiparameter joint inversion

Daniele Colombo, Ersan Turkoglu, Weichang Li, Diego Rovetta
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583272
https://doi.org/10.1190/segam2021-3583272.1
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View Paper titled, A framework for coupled physics-deep learning inversion and multiparameter joint inversion
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Proceedings Papers

Physics-constrained deep learning for ground-roll attenuation

Nam Pham, Weichang Li
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583447
https://doi.org/10.1190/segam2021-3583447.1
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Proceedings Papers

Deep network based geosteering inversion with cross-gradients

Yuchen Jin, Weichang Li
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583460
https://doi.org/10.1190/segam2021-3583460.1
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View Paper titled, Deep network based geosteering inversion with cross-gradients
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Proceedings Papers

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

Tao Lin, Mokhles Mezghani, Chicheng Xu, Weichang Li
Paper presented at the SPE Annual Technical Conference and Exhibition, September 21–23, 2021
Paper Number: SPE-206089-MS
https://doi.org/10.2118/206089-MS
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View Paper titled, Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir
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Journal Articles

Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods

Yanxiang Yu, Chicheng Xu, Siddharth Misra, Weichang Li, Michael Ashby, Wen Pan, Tianqi Deng, Honggeun Jo, Javier E. Santos, Lei Fu, Chengran Wang, Arkhat Kalbekov, Valeria Suarez, Epo Prasetya Kusumah, Mohammad Aviandito, Yogi Pamadya, Hossein Izadi
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 62 (04): 393–406.
Paper Number: SPWLA-2021-v62n4a4
https://doi.org/10.30632/PJV62N4-2021a4
Published: 01 August 2021
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Proceedings Papers

Deep Learning for Quantitative Hydraulic Fracture Profiling from Fiber Optic Measurements

Weichang Li, Han Lu, Yuchen Jin, Frode Hveding
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5583-MS
https://doi.org/10.15530/urtec-2021-5583
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View Paper titled, Deep Learning for Quantitative Hydraulic Fracture Profiling from Fiber Optic Measurements
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Proceedings Papers

Intelligent Prediction of Settling Velocity for Arbitrary Shape Particles in Vertical Fractures

Zhaopeng Zhu, Xianzhi Song, Xuezhe Yao, Shuo Zhu, Weichang Li
Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1025
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View Paper titled, Intelligent Prediction of Settling Velocity for Arbitrary Shape Particles in Vertical Fractures
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Proceedings Papers

Study on Heat Transfer Characteristics for Fluid Flow in Porous Media Based on Lattice Boltzmann Method

Weichang Li, Zhongwei Huang
Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1743
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View Paper titled, Study on Heat Transfer Characteristics for Fluid Flow in Porous Media Based on Lattice Boltzmann Method
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Proceedings Papers

Optimizing distributed acoustic sensing (DAS) acquisition: Test well design and automated data analysis

Harold Merry, Weichang Li, Max Deffenbaugh, Andrey Bakulin
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3419338
https://doi.org/10.1190/segam2020-3419338.1
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View Paper titled, Optimizing distributed acoustic sensing (DAS) acquisition: Test well design and automated data analysis
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Proceedings Papers

Physics-driven deep learning joint inversion

Daniele Colombo, Weichang Li, Diego Rovetta, Ernesto Sandoval-Curiel, Ersan Turkoglu
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424997
https://doi.org/10.1190/segam2020-3424997.1
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View Paper titled, Physics-driven deep learning joint inversion
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