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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

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

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

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 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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Proceedings Papers

A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature Oil Field in Texas

Felipe Ospina!, Pallav Sarma, Carlos Calad
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043980-MS
https://doi.org/10.15530/urtec-2024-4043980
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View Paper titled, A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature Oil Field in Texas
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Proceedings Papers

Improving Accuracy of an Auto-Forecasting Engine Using a Bayesian Maximum Likelihood Framework for Empirical Decline Curves

Doug McMaster, David S. Fulford, Stephen Krughoff, Zidong Liu, Brennan Medina
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044312-MS
https://doi.org/10.15530/urtec-2024-4044312
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View Paper titled, Improving Accuracy of an Auto-Forecasting Engine Using a Bayesian Maximum Likelihood Framework for Empirical Decline Curves
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Proceedings Papers

Predicting Gas EUR in Shale Plays Using Machine Learning Methods: A Comparative Study of Marcellus, Barnett, and Haynesville Shales

Ahmed Farid Ibrahim, Mohamed Adel Gabry, Ahmed Algarhy
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044038-MS
https://doi.org/10.15530/urtec-2024-4044038
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View Paper titled, Predicting Gas EUR in Shale Plays Using Machine Learning Methods: A Comparative Study of Marcellus, Barnett, and Haynesville Shales
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Proceedings Papers

Adapting Music Recognition Technology for Tops Picking and Quality Control

Alan Lindsey, Morgan Cox, Aaron Hugen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044181-MS
https://doi.org/10.15530/urtec-2024-4044181
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View Paper titled, Adapting Music Recognition Technology for Tops Picking and Quality Control
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Proceedings Papers

Efficient Field Development Decisions Driven by Artificial Intelligence: A Permian Basin Example

Hui Zhou, Johan A. Daal, Tom Williford, Qin Lu, Michael Burkard, Lee McAuliffe, M. D. Rincones, Rafael Paz Lopez
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044067-MS
https://doi.org/10.15530/urtec-2024-4044067
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View Paper titled, Efficient Field Development Decisions Driven by Artificial Intelligence: A Permian Basin Example
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Proceedings Papers

An Innovative Approach to Capture Depletion Impact in Unconventional Reservoir Production Prediction Using Machine Learning and a Time-Dependent Depletion Function

Edwin Ortega, Kirsten Landry
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044069-MS
https://doi.org/10.15530/urtec-2024-4044069
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View Paper titled, An Innovative Approach to Capture Depletion Impact in Unconventional Reservoir Production Prediction Using Machine Learning and a Time-Dependent Depletion Function
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