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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

Occurrence and Distribution of Antibiotics and Antibiotic Resistance Genes in the Coastal Areas of Yangtze River Estuary

Qiuhong Xu, Shuguang Liu, Sha Lou, Xin Li, Yuchen Jin, Larisa Dorzhievna Radnaeva, Elena Nikitina, Irina Viktorovna Fedorova, Makhinov Aleksei Nikolavich, José Tavares Araruna
Paper presented at the The 33rd International Ocean and Polar Engineering Conference, June 19–23, 2023
Paper Number: ISOPE-I-23-166
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View Paper titled, Occurrence and Distribution of Antibiotics and Antibiotic Resistance Genes in the Coastal Areas of Yangtze River Estuary
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Proceedings Papers

A deep learning based surrogate and uncertainty quantification for fast electromagnetic modeling in complex formations

Yuchen Jin, Chaoxian Qi, Li Yan, Yueqin Huang, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751935
https://doi.org/10.1190/image2022-3751935.1
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View Paper titled, A deep learning based surrogate and uncertainty quantification for fast electromagnetic modeling in complex formations
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Proceedings Papers

An application of FWI with progressive transfer learning

Shirui Wang, Jinjun Liu, Yuchen Jin, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3744720
https://doi.org/10.1190/image2022-3744720.1
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Proceedings Papers

Efficient progressive transfer learning for low-frequency reflection seismic data prediction

Yuchen Jin, Wenyi Hu, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594767
https://doi.org/10.1190/segam2021-3594767.1
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View Paper titled, Efficient progressive transfer learning for low-frequency reflection seismic data prediction
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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

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

RNN-based gradient prediction for solving magnetotelluric inverse problem

Yuchen Jin, Yanyan Hu, Xuqing Wu, Jiefu Chen
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3428333
https://doi.org/10.1190/segam2020-3428333.1
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View Paper titled, RNN-based gradient prediction for solving magnetotelluric inverse problem
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Proceedings Papers

A supervised descent learning technique for inversion of directional electromagnetic logging-while-drilling data

Yanyan Hu, Rui Guo, Yuchen Jin, Xuqing Wu, Maokun Li, Aria Abubakar, Jiefu Chen
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3415191
https://doi.org/10.1190/segam2020-3415191.1
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View Paper titled, A supervised descent learning technique for inversion of directional electromagnetic logging-while-drilling data
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Proceedings Papers

Physics-guided self-supervised learning for low frequency data prediction in FWI

Wenyi Hu, Yuchen Jin, Xuqing Wu, Jiefu Chen
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3423396
https://doi.org/10.1190/segam2020-3423396.1
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View Paper titled, Physics-guided self-supervised learning for low frequency data prediction in FWI
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Journal Articles

Classifying Cutting Volume at Shale Shakers in Real-Time Via Video Streaming Using Deep-Learning Techniques

Xunsheng Du, Yuchen Jin, Xuqing Wu, Yu Liu, Xianping (Sean) Wu, Omar Awan, Joey Roth, Kwee Choong See, Nicolas Tognini, Jiefu Chen, Zhu Han
Journal: SPE Drilling & Completion
Publisher: Society of Petroleum Engineers (SPE)
SPE Drill & Compl 35 (03): 317–328.
Paper Number: SPE-194084-PA
https://doi.org/10.2118/194084-PA
Published: 10 September 2020
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View article titled, Classifying Cutting Volume at Shale Shakers in Real-Time Via Video Streaming Using Deep-Learning Techniques
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Journal Articles

A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems

Yuchen Jin, Qiuyang Shen, Xuqing Wu, Jiefu Chen, Yueqin Huang
Journal: Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description
Publisher: Society of Petrophysicists and Well-Log Analysts
Petrophysics 61 (01): 86–98.
Paper Number: SPWLA-2020-v61n1a3
https://doi.org/10.30632/PJV61N1-2020a3
Published: 01 February 2020
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View article titled, A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems
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Proceedings Papers

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI

Wenyi Hu, Yuchen Jin, Xuqing Wu, Jiefu Chen
Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3216030
https://doi.org/10.1190/segam2019-3216030.1
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View Paper titled, A progressive deep transfer learning approach to cycle-skipping mitigation in FWI
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Proceedings Papers

Using a Physics-Driven Deep Neural Network to Solve Inverse Problems for LWD Azimuthal Resistivity Measurements

Yuchen Jin, Xuqing Wu, Jiefu Chen, Yueqin Huang
Paper presented at the SPWLA 60th Annual Logging Symposium, June 15–19, 2019
Paper Number: SPWLA-2019-IIII
https://doi.org/10.30632/T60ALS-2019_IIII
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View Paper titled, Using a Physics-Driven Deep Neural Network to Solve Inverse Problems for LWD Azimuthal Resistivity Measurements
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Proceedings Papers

The Embedded VGG-Net Video Stream Processing Framework for Real-Time Classification of Cutting Volume at Shale Shaker

Xunsheng Du, Yuchen Jin, Xuqing Wu, Yu Liu, Xianping Wu, Omar Awan, Joey Roth, Kwee Choong See, Nicolas Tognini, Jiefu Chen, Zhu Han
Paper presented at the International Petroleum Technology Conference, March 26–28, 2019
Paper Number: IPTC-19312-MS
https://doi.org/10.2523/IPTC-19312-MS
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View Paper titled, The Embedded VGG-Net Video Stream Processing Framework for Real-Time Classification of Cutting Volume at Shale Shaker
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Proceedings Papers

Deep Learning Model for Classifying Cutting Volume at Shale Shakers in Real-Time via Video Streaming

Xunsheng Du, Yuchen Jin, Xuqing Wu, Yu Liu, Xianping Wu, Omar Awan, Joey Roth, Kwee Choong See, Nicolas Tognini, Jiefu Chen, Zhu Han
Paper presented at the SPE/IADC International Drilling Conference and Exhibition, March 5–7, 2019
Paper Number: SPE-194084-MS
https://doi.org/10.2118/194084-MS
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View Paper titled, Deep Learning Model for Classifying Cutting Volume at Shale Shakers in Real-Time via Video Streaming
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Proceedings Papers

Learn low wavenumber information in FWI via deep inception based convolutional networks

Yuchen Jin, Wenyi Hu, Xuqing Wu, Jiefu Chen
Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-2997901
https://doi.org/10.1190/segam2018-2997901.1
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View Paper titled, Learn low wavenumber information in FWI via deep inception based convolutional networks
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Proceedings Papers

Seismic data denoising by deep-residual networks

Yuchen Jin, Xuqing Wu, Jiefu Chen, Zhu Han, Wenyi Hu
Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-2998619
https://doi.org/10.1190/segam2018-2998619.1
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View Paper titled, Seismic data denoising by deep-residual networks
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