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Proceedings Papers

Hyperspectral Image Segmentation of Methane Plumes Using Large Machine Learning Model

Yuan Zi, Jiefu Chen, Xuqing Wu, Lei Fan, Zhu Han
Paper presented at the SPE Annual Technical Conference and Exhibition, September 23–25, 2024
Paper Number: SPE-221013-MS
https://doi.org/10.2118/221013-MS
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Proceedings Papers

Segmenting hyperspectral images of methane plumes with a large machine learning model

Yuan Zi, Lei Fan, Xuqing Wu, Jiefu Chen, Zhu Han
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4099656
https://doi.org/10.1190/image2024-4099656.1
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Journal Articles

Deep Learning Method for Improving Rate of Penetration Prediction in Drilling

Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
Journal: SPE Journal
Publisher: Society of Petroleum Engineers (SPE)
SPE J. 29 (07): 3440–3448.
Paper Number: SPE-219746-PA
https://doi.org/10.2118/219746-PA
Published: 10 July 2024
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View article titled, Deep Learning Method for Improving Rate of Penetration Prediction in Drilling
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Proceedings Papers

Predicting Coiled-Tubing Drilling Dynamics Using Transformers

Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043196-MS
https://doi.org/10.15530/urtec-2024-4043196
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View Paper titled, Predicting Coiled-Tubing Drilling Dynamics Using Transformers
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Proceedings Papers

A Deep Learning Approach to Predicting Remaining Useful Life for Downhole Drilling Sensors Using Synthetic Data Generation

Aamir Bader Shah, Yu Wen, Jiefu Chen, Xuqing Wu, Xin Fu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4046687-MS
https://doi.org/10.15530/urtec-2024-4046687
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View Paper titled, A Deep Learning Approach to Predicting Remaining Useful Life for Downhole Drilling Sensors Using Synthetic Data Generation
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Proceedings Papers

Seismic Data Compression by Variational Autoencoder With Hyperprior

Shirui Wang, Wenyi Hu, Aria Abubakar, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3910119
https://doi.org/10.1190/image2023-3910119.1
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Proceedings Papers

Microseismic sensor placement optimization for rapid leakage alarm of geologic carbon storage

Yuan Zi, Lei Fan, Xuqing Wu, Jiefu Chen, Zhu Han
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3910000
https://doi.org/10.1190/image2023-3910000.1
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View Paper titled, Microseismic sensor placement optimization for rapid leakage alarm of geologic carbon storage
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Proceedings Papers

Transformer-based deep learning model for accurate rate of penetration prediction in drilling

Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3910241
https://doi.org/10.1190/image2023-3910241.1
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View Paper titled, Transformer-based deep learning model for accurate rate of penetration prediction in drilling
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Proceedings Papers

Deep Learning Enhanced Joint Inversion for Mineral Exploration Using Airborne Geophysics: Application in Decorah Area

Yanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Yueqin Huang
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3910948
https://doi.org/10.1190/image2023-3910948.1
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View Paper titled, Deep Learning Enhanced Joint Inversion for Mineral Exploration Using Airborne Geophysics: Application in Decorah Area
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Proceedings Papers

Anti-aliasing seismic data interpolation by dip-informed self-supervised learning

Shirui Wang, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3913209
https://doi.org/10.1190/image2023-3913209.1
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View Paper titled, Anti-aliasing seismic data interpolation by dip-informed self-supervised learning
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Proceedings Papers

Robotic Fish Enabled Offshore Pipeline Inspection

Xuqing Wu, Tushar Sood, Zheng Chen, Jiefu Chen
Paper presented at the Offshore Technology Conference, May 1–4, 2023
Paper Number: OTC-32427-MS
https://doi.org/10.4043/32427-MS
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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

Self-supervised learning for seismic swell noise removal

Yuan Zi, Shirui Wang, Pengyu Yuan, Xuqing Wu, Jiefu Chen, Zhu Han
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745315
https://doi.org/10.1190/image2022-3745315.1
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View Paper titled, Self-supervised learning for seismic swell noise removal
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Proceedings Papers

Active gamma-ray well logging pattern localization with reinforcement learning

Yuan Zi, Lei Fan, Xuqing Wu, Jiefu Chen, Shirui Wang, Zhu Han
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745281
https://doi.org/10.1190/image2022-3745281.1
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View Paper titled, Active gamma-ray well logging pattern localization with reinforcement learning
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Proceedings Papers

Deep-learning-based downhole tool electromagnetic telemetry noise attenuation and source separation

Carlos Urdaneta, Arnaud Jarrot, Shirui Wang, Xuqing Wu, Jiefu Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745847
https://doi.org/10.1190/image2022-3745847.1
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View Paper titled, Deep-learning-based downhole tool electromagnetic telemetry noise attenuation and source separation
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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

A flexible and versatile joint inversion framework using deep learning

Yanyan Hu, Jiefu Chen, Xuqing Wu, Yueqin Huang
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3728722
https://doi.org/10.1190/image2022-3728722.1
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Proceedings Papers

Physics-guided self-supervised learning for monochromatic noise removal

Yuan Zi, Wenyi Hu, Jiefu Chen, Xuqing Wu, Zhu Han
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583068
https://doi.org/10.1190/segam2021-3583068.1
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View Paper titled, Physics-guided self-supervised learning for monochromatic noise removal
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Proceedings Papers

Deep learning-enhanced multiphysics joint inversion

Yanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Jiuping Chen, Yueqing Huang
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583667
https://doi.org/10.1190/segam2021-3583667.1
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Proceedings Papers

Seismic deblending by self-supervised deep learning with a blind-trace network

Shirui Wang, Wenyi Hu, Pengyu Yuan, Xuqing Wu, Qunshan Zhang, Prashanth Nadukandi, German Ocampo Botero, Jiefu Chen
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583662
https://doi.org/10.1190/segam2021-3583662.1
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View Paper titled, Seismic deblending by self-supervised deep learning with a blind-trace network
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