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Proceedings Papers

Enhanced Decision Making and Asset Optimization for Unconventional Resources with Type Wells (DCA), RTA-Based Numerical Modeling and Machine Learning

Alireza Haghighat, Toby Burrough
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044552-MS
https://doi.org/10.15530/urtec-2024-4044552
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View Paper titled, Enhanced Decision Making and Asset Optimization for Unconventional Resources with Type Wells (DCA), RTA-Based Numerical Modeling and Machine Learning
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Proceedings Papers

Convolutional Neural Networks Forecasting for Unconventional Drilling Units for US Land

Francisco J. Parga Garcia, Jie Fang, Niven Shumaker
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044012-MS
https://doi.org/10.15530/urtec-2024-4044012
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View Paper titled, Convolutional Neural Networks Forecasting for Unconventional Drilling Units for US Land
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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

Experimental Studies of Pore Connectivity, Liquid Imbibition, and Gas Diffusivity in Niobrara Formation, Denver Basin, Colorado

Ogochukwu Azike, Qinhong Hu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043707-MS
https://doi.org/10.15530/urtec-2024-4043707
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View Paper titled, Experimental Studies of Pore Connectivity, Liquid Imbibition, and Gas Diffusivity in Niobrara Formation, Denver Basin, Colorado
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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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Proceedings Papers

Analytical and Machine Learning Based Modifications to Unconventional Reservoir Simulation Models to Capture Near-Fracture Transient Effects

Hector E. Barrios-Molano, Alvaro Rey, Shihao Wang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044074-MS
https://doi.org/10.15530/urtec-2024-4044074
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View Paper titled, Analytical and Machine Learning Based Modifications to Unconventional Reservoir Simulation Models to Capture Near-Fracture Transient Effects
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Proceedings Papers

Improved Unconventional Development Decisions Using Causal Inference

Henry Galvis Silva, Oliver Rojas Conde, Sebastien Matringe
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044399-MS
https://doi.org/10.15530/urtec-2024-4044399
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View Paper titled, Improved Unconventional Development Decisions Using Causal Inference
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Proceedings Papers

Data-Driven Risking for Induced Seismicity

Mariana Rodriguez-Buno, Yang Chen, Stefan Hussenoeder
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044665-MS
https://doi.org/10.15530/urtec-2024-4044665
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View Paper titled, Data-Driven Risking for Induced Seismicity
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Proceedings Papers

Evaluation of Chemical Additives Impact on Well Productivity by Using Machine Learning Algorithms

Sohrat Baki, Serkan Dursun, Najd Alotaibi
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044387-MS
https://doi.org/10.15530/urtec-2024-4044387
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View Paper titled, Evaluation of Chemical Additives Impact on Well Productivity by Using Machine Learning Algorithms
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Proceedings Papers

Autoforecasting and Minimizing Aggregate Errors in Large Datasets - A Pinedale Field Case Study

Brooks Black, Darin Oberg, Changjae Kim, Brodee Lee, Allyson Kidwell, Adrian Morales
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044112-MS
https://doi.org/10.15530/urtec-2024-4044112
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View Paper titled, Autoforecasting and Minimizing Aggregate Errors in Large Datasets - A Pinedale Field Case Study
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Proceedings Papers

Evaluation of Empirical Correlations and Time Series Models for the Prediction and Forecast of Unconventional Wells Production in Wolfcamp A Formation

Aimen Laalam, Houdaifa Khalifa, Habib Ouadi, Mouna Keltoum Benabid, Olusegun Stanley Tomomewo, Mouad Al Krmagi
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043738-MS
https://doi.org/10.15530/urtec-2024-4043738
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View Paper titled, Evaluation of Empirical Correlations and Time Series Models for the Prediction and Forecast of Unconventional Wells Production in Wolfcamp A Formation
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Proceedings Papers

Innovating Oil and Gas Forecasting: Developing a Trailblazing Generative AI Model

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-4044635-MS
https://doi.org/10.15530/urtec-2024-4044635
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View Paper titled, Innovating Oil and Gas Forecasting: Developing a Trailblazing Generative AI Model
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Proceedings Papers

Industry Consortium Insights: Benchmarking Bottomhole Pressure Calculations in Tight Unconventionals

M. Carlsen, A. Proaño, V. Chavali, C. H. Whitson, M. M. Dahouk, S. Mydland, S. Liem, N. Longenbaugh, R. Sinha, J. Billings, D. Axelson, A. Lerza, B. Collins, N. Lightner, J. V. Dijken, I. Arguello, M. Watson
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4045619-MS
https://doi.org/10.15530/urtec-2024-4045619
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View Paper titled, Industry Consortium Insights: Benchmarking Bottomhole Pressure Calculations in Tight Unconventionals
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Proceedings Papers

Boosting Visualization of Image Logs in 3D Towards Automated Well-Centric Interpretation

Eya Ghamgui, Chandramani Shrivastava, Martin Carles, Josselin Kherroubi
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4054007-MS
https://doi.org/10.15530/urtec-2024-4054007
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View Paper titled, Boosting Visualization of Image Logs in 3D Towards Automated Well-Centric Interpretation
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Proceedings Papers

Using Machine Learning to Automate FDI Analysis

Reid Thompson, Lance Legel, Thomas Hanlon
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4045912-MS
https://doi.org/10.15530/urtec-2024-4045912
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Proceedings Papers

Analyzing Midland Basin Well Performance and Future Outlook with Machine Learning

Brandon Myers, Ted Cross, Alexander Cui
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044737-MS
https://doi.org/10.15530/urtec-2024-4044737
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View Paper titled, Analyzing Midland Basin Well Performance and Future Outlook with Machine Learning
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Proceedings Papers

Enhancing Production Forecasting Accuracy: A Machine Learning Approach in the Permian Basin

Kiran Sathaye, Scott Iceton, Ted Cross
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044746-MS
https://doi.org/10.15530/urtec-2024-4044746
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View Paper titled, Enhancing Production Forecasting Accuracy: A Machine Learning Approach in the Permian Basin
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