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Proceedings Papers

Precursory Detection of Casing Deformation and Induced Seismicity in Unconventional Reservoirs, via Real-Time Surface Pressure Data Analytics

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Thomas de Boer, Matthew Adams, Andrew McMurray, Giovanni Grasselli
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4266758-MS
https://doi.org/10.15530/urtec-2025-4266758
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View Paper titled, Precursory Detection of Casing Deformation and Induced Seismicity in Unconventional Reservoirs, via Real-Time Surface Pressure Data Analytics
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Proceedings Papers

Synthetic Data-Driven Hybrid Deep Learning Methodology for Predicting Remaining Useful Life of Sensors in Downhole Drilling Equipment

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Aamir Bader Shah, Yu Wen, Jiefu Chen, Xuqing Wu, Renjie Hu, Xin Fu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4207580-MS
https://doi.org/10.15530/urtec-2025-4207580
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View Paper titled, Synthetic Data-Driven Hybrid Deep Learning Methodology for Predicting Remaining Useful Life of Sensors in Downhole Drilling Equipment
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Proceedings Papers

Cross-Basin Analysis of Production Drivers: Insight from Machine Learning Applied to 100,000 Unconventional Wells

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Ben Davis, Dillon Niederhut
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4214380-MS
https://doi.org/10.15530/urtec-2025-4214380
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View Paper titled, Cross-Basin Analysis of Production Drivers: Insight from Machine Learning Applied to 100,000 Unconventional Wells
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Proceedings Papers

Machine-Learning Driven Prediction of Geological Marker Signatures

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Shashin Sharan, Kaustubh Shrivastava, Per Irgens, Tatjana Scherschel
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4234773-MS
https://doi.org/10.15530/urtec-2025-4234773
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View Paper titled, Machine-Learning Driven Prediction of Geological Marker Signatures
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Proceedings Papers

A Sequential Machine Learning Framework for Individual Well Forecasting in the Bakken

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Ahmed G. Almetwally, Alisha Meether, Christian Gronister, Taylor Goy, Amir Kianinejad, Sebastien Matringe
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4242604-MS
https://doi.org/10.15530/urtec-2025-4242604
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View Paper titled, A Sequential Machine Learning Framework for Individual Well Forecasting in the Bakken
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Proceedings Papers

Detecting Resistivity Tool Malfunctions in Coiled Tubing Drilling Using Generative Artificial Intelligence

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Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4247193-MS
https://doi.org/10.15530/urtec-2025-4247193
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View Paper titled, Detecting Resistivity Tool Malfunctions in Coiled Tubing Drilling Using Generative Artificial Intelligence
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Proceedings Papers

Machine Learning Methods for Handling Parameter Space Sampling Bias in Unconventional Well Performance Prediction

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Oliver Rojas Conde, Henry Galvis Silva, Sebastien Matringe
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4249754-MS
https://doi.org/10.15530/urtec-2025-4249754
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View Paper titled, Machine Learning Methods for Handling Parameter Space Sampling Bias in Unconventional Well Performance Prediction
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Proceedings Papers

Enhancing Lithology Classification Through a Deep Learning Framework

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P. Zhang, T. Gao, R. Li
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4252996-MS
https://doi.org/10.15530/urtec-2025-4252996
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View Paper titled, Enhancing Lithology Classification Through a Deep Learning Framework
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Proceedings Papers

Quantifying Spacing Degradation in Northern Delaware Basin Using Machine Learning and Subsurface Properties

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Charles Connell, Kyle LaMotta, Jizhou Li, Matt Mayer
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4254307-MS
https://doi.org/10.15530/urtec-2025-4254307
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View Paper titled, Quantifying Spacing Degradation in Northern Delaware Basin Using Machine Learning and Subsurface Properties
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Proceedings Papers

Evaluating DSU Orientation in the Bakken Basin: Integrating Machine Learning and Physics-Based Models for Optimal Development

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Miguel Jakymec, Amir Kianinejad, Michael McKimmy, Carlos Miranda, Sebastien Matringe
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4257170-MS
https://doi.org/10.15530/urtec-2025-4257170
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View Paper titled, Evaluating DSU Orientation in the Bakken Basin: Integrating Machine Learning and Physics-Based Models for Optimal Development
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Proceedings Papers

High-Resolution DFN Modeling via Seismic Attribute Integration in the Sichuan Basin for Completion Optimization

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Xuefeng Yang, Shengxian Zhao, Dongchen Liu, Deliang Zhang, Lieyan Cao, Joseph Leines Artieda, Chuxi Liu, Wei Yu, Jijun Miao
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4257601-MS
https://doi.org/10.15530/urtec-2025-4257601
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View Paper titled, High-Resolution DFN Modeling via Seismic Attribute Integration in the Sichuan Basin for Completion Optimization
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Proceedings Papers

Large Language Model Powered Well Productivity

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Sohrat Baki, Serkan Dursun
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4259056-MS
https://doi.org/10.15530/urtec-2025-4259056
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View Paper titled, Large Language Model Powered Well Productivity
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Proceedings Papers

Advancing Formation Tops Fingerprinting: Expanding Applications and Insights from the DJ to the Williston Basin

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Alan Lindsey, Morgan Cox, J. Alexandra Sloan, James Hawkins, Clayton Mack
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4259296-MS
https://doi.org/10.15530/urtec-2025-4259296
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View Paper titled, Advancing Formation Tops Fingerprinting: Expanding Applications and Insights from the DJ to the Williston Basin
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Proceedings Papers

Integrating Deep Learning and Seismic Data for Mudstone Characterization and SAGD Development in Heterogeneous Reservoirs

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Huiwen Pang, Hanqing Wang, Chuan Qin, Jingwei Tian
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4261039-MS
https://doi.org/10.15530/urtec-2025-4261039
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View Paper titled, Integrating Deep Learning and Seismic Data for Mudstone Characterization and SAGD Development in Heterogeneous Reservoirs
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Proceedings Papers

Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment

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Rajkanwar Singh, Saaksshi Jilhewar, Audrey Der, Ryan Mercer, Vikram Jayaram
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4261542-MS
https://doi.org/10.15530/urtec-2025-4261542
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View Paper titled, Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment
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Proceedings Papers

Using Machine Learning to Forecast Longer Duration Inventory Across Rockies Unconventional Plays

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Brandon Myers, Gabriel Quintero
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4263701-MS
https://doi.org/10.15530/urtec-2025-4263701
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View Paper titled, Using Machine Learning to Forecast Longer Duration Inventory Across Rockies Unconventional Plays
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Proceedings Papers

Can Transfer Learning be Used to Forecast Production in Frontier Basins? A Case Study from the Powder River Basin

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Dillon Niederhut, Gabriel A. Quintero
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4264880-MS
https://doi.org/10.15530/urtec-2025-4264880
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View Paper titled, Can Transfer Learning be Used to Forecast Production in Frontier Basins? A Case Study from the Powder River Basin
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Proceedings Papers

PHREEQC-Informed Machine Learning for Predicting Scaling Indices in Permian Basin Produced Waters

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Claudia Molina, Iván Darío Piñerez Torrijos, Mohanad Hussein Gibreel
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4265270-MS
https://doi.org/10.15530/urtec-2025-4265270
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View Paper titled, PHREEQC-Informed Machine Learning for Predicting Scaling Indices in Permian Basin Produced Waters
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Proceedings Papers

Harnessing AI and Computer Vision for Efficient Geothermal Field Exploration and Prospect Evaluation

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Moamen Gasser, Danny Rehg, Marcus Oesterberg, Nathan Meehan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4265623-MS
https://doi.org/10.15530/urtec-2025-4265623
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View Paper titled, Harnessing AI and Computer Vision for Efficient Geothermal Field Exploration and Prospect Evaluation
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Proceedings Papers

Permian Delaware Basin Productivity and Spacing Analysis and Technically Recoverable Resource Assessment

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Sofia Berdysheva, Timothy McMahon, David Hoffman
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 9–11, 2025
Paper Number: URTEC-4265714-MS
https://doi.org/10.15530/urtec-2025-4265714
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View Paper titled, Permian Delaware Basin Productivity and Spacing Analysis and Technically Recoverable Resource Assessment
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