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Proceedings Papers

Mapping Oil-Prone Facies in 3D for Field Development and Optimizing Production: A Midland Basin Case Study

Paritosh Bhatnagar, Venkatesh Anantharamu, Ron Bianco
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4020437-MS
https://doi.org/10.15530/urtec-2024-4020437
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View Paper titled, Mapping Oil-Prone Facies in 3D for Field Development and Optimizing Production: A Midland Basin Case Study
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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

Estimation of Stimulated Reservoir Region for Hydraulic Fracturing in Shale Gas Well Based on Ensemble Learning Algorithm

Yang Luo, Bo Kang, Yan Feng, Hehua Wang, Zhongrong Mi, Yi Cheng, Yong Xiao, Xing Zhao, Jianchun Guo, Cong Lu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4018039-MS
https://doi.org/10.15530/urtec-2024-4018039
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View Paper titled, Estimation of Stimulated Reservoir Region for Hydraulic Fracturing in Shale Gas Well Based on Ensemble Learning Algorithm
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Proceedings Papers

Optimization of Parent-Child Well Spacing and Completion Design: A Delaware Basin Case Study

Hongjie Xiong, Prakhar Sarkar, Sangcheol Yoon
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4023356-MS
https://doi.org/10.15530/urtec-2024-4023356
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View Paper titled, Optimization of Parent-Child Well Spacing and Completion Design: A Delaware Basin Case Study
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Proceedings Papers

Efficient Asset Optimization with Hybrid Models: Combining Surface Network and Reduced Physics Reservoir Models

Zhenyu Guo, Sathish Sankaran
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4036174-MS
https://doi.org/10.15530/urtec-2024-4036174
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View Paper titled, Efficient Asset Optimization with Hybrid Models: Combining Surface Network and Reduced Physics Reservoir Models
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Proceedings Papers

Real Time Data Driven Framework for Rate of Penetration Optimization of S-Shaped Wells in a Southern Iraq Field Using Prior Knowledge

Ethar H. AlKamil, Atheer Alattar, Mahdi Karnot, Muntadhar Talib, Abdulla Mazin, Salam Taher
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043246-MS
https://doi.org/10.15530/urtec-2024-4043246
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View Paper titled, Real Time Data Driven Framework for Rate of Penetration Optimization of S-Shaped Wells in a Southern Iraq Field Using Prior Knowledge
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Proceedings Papers

Physics Informed Deep Learning Models for Improving Shale and Tight Forecast Scalability and Reliability

Kainan Wang, Lichi Deng, Yuzhe Cai, Guido Di Federico, Keith Ramsaran, Mun Hong Hui, Hussein Alboudwarej, Christian Hager, Yuguang Chen, Xian-Huan Wen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4042557-MS
https://doi.org/10.15530/urtec-2024-4042557
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View Paper titled, Physics Informed Deep Learning Models for Improving Shale and Tight Forecast Scalability and Reliability
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Proceedings Papers

A Machine-Learning-Based Workflow for Drilling Risk Prediction of Wellbore Instability and Trajectory Optimization in Ultra-Deep Formation

Hanqing Wang, Ruxin Zhang, Ziyan Deng, Jin Meng, Han Wang, Yujie Zhou, Fan Yang, Ji Chang, Yitian Xiao, Huiwen Pang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4034008-MS
https://doi.org/10.15530/urtec-2024-4034008
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View Paper titled, A Machine-Learning-Based Workflow for Drilling Risk Prediction of Wellbore Instability and Trajectory Optimization in Ultra-Deep Formation
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Proceedings Papers

Physics-Informed Machine Learning Approach for Closed-Loop Reservoir Management Using RGNet

Zhenyu Guo, Sathish Sankaran
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4036015-MS
https://doi.org/10.15530/urtec-2024-4036015
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View Paper titled, Physics-Informed Machine Learning Approach for Closed-Loop Reservoir Management Using RGNet
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Proceedings Papers

Section-Based Data Analytics to Optimize Unconventional Development and Completions

Jesse Rossman, Karn Agarwal, Howard Melcher, Mike Mayerhofer
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4042757-MS
https://doi.org/10.15530/urtec-2024-4042757
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View Paper titled, Section-Based Data Analytics to Optimize Unconventional Development and Completions
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Proceedings Papers

Machine Learning vs. Type Curves in the Appalachian Basin: A Comparative Study

A. Cui, A. Yanke, T. Dao, P. Ye, T. Cross, B. Davis
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4036239-MS
https://doi.org/10.15530/urtec-2024-4036239
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View Paper titled, Machine Learning vs. Type Curves in the Appalachian Basin: A Comparative Study
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Proceedings Papers

Smart Control: Advancing the Optimization and Control of Artificial Lift Systems

Sam Bost, Jackson Searle
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4031362-MS
https://doi.org/10.15530/urtec-2024-4031362
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View Paper titled, Smart Control: Advancing the Optimization and Control of Artificial Lift Systems
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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

Transforming Early-Stage Oil and Gas Production Forecasting with Generative AI

Akash Sharma, Scott Pellegrino, Sai Sidharth Manikandan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043583-MS
https://doi.org/10.15530/urtec-2024-4043583
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View Paper titled, Transforming Early-Stage Oil and Gas Production Forecasting with Generative AI
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Proceedings Papers

Towards Universal Production Forecasting via Adversarial Transfer Learning and Transformer with Application in the Shengli Oilfield, China

Ji Chang, Jin Meng, Dongwei Zhang, Tianrui Ye, Han Wang, Yitian Xiao
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4032318-MS
https://doi.org/10.15530/urtec-2024-4032318
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View Paper titled, Towards Universal Production Forecasting via Adversarial Transfer Learning and Transformer with Application in the Shengli Oilfield, China
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Proceedings Papers

A Machine Learning Approach to Basin Scale Inventory Forecasting and Development Planning that Accounts for Complex Well Interactions

Charles Connell, Kyle LaMotta, Clark Munger, Joe Wicker
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4036541-MS
https://doi.org/10.15530/urtec-2024-4036541
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View Paper titled, A Machine Learning Approach to Basin Scale Inventory Forecasting and Development Planning that Accounts for Complex Well Interactions
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Proceedings Papers

Machine Learning-Based Sweet Spot Prediction Method for Canada Tight Sandstone Gas Reservoir

Zan Chen, Yexin Fang, Qian Yuan, Hao Su, Yili Yao, Yu Chen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4025206-MS
https://doi.org/10.15530/urtec-2024-4025206
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View Paper titled, Machine Learning-Based Sweet Spot Prediction Method for Canada Tight Sandstone Gas Reservoir
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Proceedings Papers

Practical Models for Computing CO 2 Solubility in Brines with Complex Ions for Carbon Geo-Sequestration Applications

Gideon Gyamfi, David Kwaku Danso, Xiaoli Li
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4033482-MS
https://doi.org/10.15530/urtec-2024-4033482
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View Paper titled, Practical Models for Computing CO 2 Solubility in Brines with Complex Ions for Carbon Geo-Sequestration Applications
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Proceedings Papers

A Physics-Based Approach to Characterize Productivity Loss in the Haynesville Shale

Chad Jongeling, Jerrod Ryan, Christopher Ponners, Dominick Wytovich, Joe Miller, Josh Jackson, Mark McClure, Garrett Fowler
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4033310-MS
https://doi.org/10.15530/urtec-2024-4033310
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View Paper titled, A Physics-Based Approach to Characterize Productivity Loss in the Haynesville Shale
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Proceedings Papers

A Comparative Analysis of Machine Learning Techniques for Geothermal Wells' Drilling Rate of Penetration (ROP) Prediction

Taha Yehia, Moamen Gasser, Hossam Ebaid, Nathan Meehan, Esuru Rita Okoroafor
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044244-MS
https://doi.org/10.15530/urtec-2024-4044244
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View Paper titled, A Comparative Analysis of Machine Learning Techniques for Geothermal Wells' Drilling Rate of Penetration (ROP) Prediction
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