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Proceedings Papers

Application of Deep Learning Neural Networks to Petrophysical Property Prediction: Case Study from the Powder River Basin

Cameron Snow
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702980-MS
DOI: https://doi.org/10.15530/urtec-2022-3702980
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Prediction and Analysis of Geomechanical Properties using Deep Learning: A Permian Basin Case Study

Fatick Nath, Karina Murillo, Sarker Monojit Asish, Deepak Ganta, Valeria Limon, Edgardo Aguirre, Gabriel Aguirre, Happy R. Debi, Jose L. Perez, Cesar Netro, Flavio Borjas
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3707202-MS
DOI: https://doi.org/10.15530/urtec-2022-3707202
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Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702606-MS
DOI: https://doi.org/10.15530/urtec-2022-3702606
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Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs

Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib Asl
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703284-MS
DOI: https://doi.org/10.15530/urtec-2022-3703284
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Sequence-to-Sequence (Seq2Seq) Long Short-Term Memory (LSTM) for Oil Production Forecast of Shale Reservoirs

Cristhian Aranguren, Alfonso Fragoso, Roberto Aguilera
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722179-MS
DOI: https://doi.org/10.15530/urtec-2022-3722179
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Viable Solutions to Overcome Weaknesses of Deep Learning Applications in Production Forecasting: A Comprehensive Review

Y. Kocoglu, S. Gorell
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721904-MS
DOI: https://doi.org/10.15530/urtec-2022-3721904
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Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring

Guoxiang Liu, Abhash Kumar, Song Zhao, Chung Yan Shih, Veronika Vasylkivska, Paul Holcomb, Richard Hammack, Jeffery Ilconich, Grant Bromhal
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723466-MS
DOI: https://doi.org/10.15530/urtec-2022-3723466
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Forecasting Well-to-Well Interference in the Permian Basin by Applying Neural Networks on Pre-Run Simulations

Yogashri Pradhan, W. John Lee, Duc Lam, Kyle Hanna, Thuy Nguyen, Andrew (Quang) Tran
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723394-MS
DOI: https://doi.org/10.15530/urtec-2022-3723394
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Machine Learning Applications for Reservoir Pressure Trend Prediction in Potash Area of Delaware Basin

Olabode Thomas Ajibola, James Sheng, Phillip McElroy, Ebru Unal
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
DOI: https://doi.org/10.15530/urtec-2022-3722944
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Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence

Shane Quimby, Yanwei Zhao, Jie Zhang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723688-MS
DOI: https://doi.org/10.15530/urtec-2022-3723688
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A Deep Learning Framework for Multi-Horizon Probabilistic Production Forecasting in Unconventional Reservoirs

Rohan Thavarajah, Hamed Darabi, Nick Ruta, Amir Salehi, Amir Kianinejad
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723682-MS
DOI: https://doi.org/10.15530/urtec-2022-3723682
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Improved UCR Development Decision through Probabilistic Modeling with Convolutional Neural Network

Han Young Park, Yunhui Tan, Baosheng Liang, Yuguang Chen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723793-MS
DOI: https://doi.org/10.15530/urtec-2022-3723793
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Deep-Learning-Based Prediction of Post-Fracturing Permeability Field for Development Strategy Optimization in Unconventional Reservoirs

Jiehao Wang, Yunhui Tan, Baosheng Liang, Xinhui Min, Chaoshun Hu, Chao Zhao, Yuyu Wang, Mike Li, Yuguang Chen, Gerardo Jimenez, Shahzad Khan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723603-MS
DOI: https://doi.org/10.15530/urtec-2022-3723603
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Transfer Learning with Multiple Aggregated Source Models in Unconventional Reservoirs

J. Cornelio, S. Mohd Razak, Y. Cho, H.-H. Liu, R. Vaidya, B. Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723965-MS
DOI: https://doi.org/10.15530/urtec-2022-3723965
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Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Hanyan Zhang, Max T. Kasumov, Deepak Devegowda, Mark E. Curtis
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5562-MS
DOI: https://doi.org/10.15530/urtec-2021-5562
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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
DOI: https://doi.org/10.15530/urtec-2021-5583
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Transfer Learning with Recurrent Neural Networks for Long-term Production Forecasting in Unconventional Reservoirs

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5687-MS
DOI: https://doi.org/10.15530/urtec-2021-5687
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Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs

J. Cornelio, S. Mohd Razak, A. Jahandideh, Y. Cho, H-H. Liu, R. Vaidya, B. Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5688-MS
DOI: https://doi.org/10.15530/urtec-2021-5688
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Discovery Science of Hydraulic Fracturing and Shale Fundamentals

Mohamed Mehana, Javier E. Santos, Chelsea Neil, Matthew R. Sweeney, Jeffery Hyman, Satish Karra, Hongwu Xu, Qinjun Kang, James William Carey, George Guthrie, Hari Viswanathan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5406-MS
DOI: https://doi.org/10.15530/urtec-2021-5406
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Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach

Baosen Zhang, Shiwang Chen, Yitian Xiao, Laiming Zhang, Chengshan Wang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5375-MS
DOI: https://doi.org/10.15530/urtec-2021-5375
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