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Proceedings Papers

Midland Basin Wolfcamp Resource Assessment Using Well Spacing Normalized Recovery per Section

Dicman Alfred, John McLaughlin
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3707494-MS
DOI: https://doi.org/10.15530/urtec-2022-3707494
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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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An Innovative and Simple Approach to Spatially Evaluate a Proxy for Stress Shadow Effect of Unconventional Reservoir Completion Activity

Shane J. Prochnow, Alena Grechishnikova, Paymon Hossaini, Mohamed Ibrahim, Sandra Saldana
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3713519-MS
DOI: https://doi.org/10.15530/urtec-2022-3713519
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Application of Unsupervised-Manifold-Learning for Productivity Driver Evaluation and Production Forecasting in Unconventional Oil Reservoirs

Yang Gao, Yuewei Pan, Jianhua Qin, Enbo Qiu, Wei Ma, Ran Bi
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3713006-MS
DOI: https://doi.org/10.15530/urtec-2022-3713006
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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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From WHP to BHP Using Machine Learning in Multi-Fractured Horizontal Wells of the Vaca Muerta Formation

Agustín Terminiello, Matías Nasca, Juan Filipich, Donald Mc Intyre, Pablo Crespo
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703282-MS
DOI: https://doi.org/10.15530/urtec-2022-3703282
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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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Optimizing Completions Strategies Using Low-Cost Data Learning

Antoine Jacques, Clement Daguet-Schott, Anthony Billat, Benoit Brouard
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3701794-MS
DOI: https://doi.org/10.15530/urtec-2022-3701794
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Quantifying Proppant in the SRV Along a Fractured Well at the Hydraulic Fracturing Test Site: A Proppant Log Case Study from Permian-Delaware Basin

Debotyam Maity, Jordan Ciezobka
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3704106-MS
DOI: https://doi.org/10.15530/urtec-2022-3704106
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Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

Carolan Laudon, Jie Qi, Yin-Kai Wang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3701806-MS
DOI: https://doi.org/10.15530/urtec-2022-3701806
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An Unsupervised Stochastic Machine Learning Approach for Well Log Outlier Identification

Ridvan Akkurt, Bhuvaneswari Sankaranarayanan, Vanessa Simoes, Hiren Maniar, Per Irgens, Ben Hoffman, Kevin Fisher
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721358-MS
DOI: https://doi.org/10.15530/urtec-2022-3721358
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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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Automated Well Top Picking and Reservoir Property Analysis of the Belly River Formation of the Western Canada Sedimentary Basin

Baosen Zhang, Tianrui Ye, Yitian Xiao, Dongmei Li, Guoping Wang, Cong Su, Tongyun Yao
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719133-MS
DOI: https://doi.org/10.15530/urtec-2022-3719133
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An Accurate and Efficient Fracture Propagation Model Auto-Calibration Workflow for Unconventional Reservoirs

Chuxi Liu, Kan Wu, Jianfa Wu, Cheng Chang, Xuefeng Yang, Yiwen Gong, Edward Zhao, Wei Yu, Kamy Sepehrnoori
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3720957-MS
DOI: https://doi.org/10.15530/urtec-2022-3720957
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Natural Fracture Presence Prediction in Unconventional Reservoirs Using Machine Learning and Geostatistical Methods - Workflow and HFTS1 Case

Peace C. Eze, Lin Y. Hu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721712-MS
DOI: https://doi.org/10.15530/urtec-2022-3721712
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Discovering Subsurface "Oceans" with Classification: A Machine Learning Method to Predict Drilling Challenges from Nearby Saltwater Disposal Wells

Justin Kilb, Brandon Brame
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722093-MS
DOI: https://doi.org/10.15530/urtec-2022-3722093
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Integration of Machine Learning and Numerical Simulation to Estimate Child Well Depletion

Edward Wolfram, James Cassanelli, Soodabeh Esmaili, Hui (Jack) Deng, Vivek Muralidharan, Iwan Harmawan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719366-MS
DOI: https://doi.org/10.15530/urtec-2022-3719366
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Machine-Learning-Based Completion Optimization Workflow for Multi-Bench Development in the Midland Basin

Jaeyoung Park, Trent Wien, Kyle Smith, Chuck Nwabuoku, Raquel Gordillo
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719984-MS
DOI: https://doi.org/10.15530/urtec-2022-3719984
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Real Time Torque and Drag Analysis by Combining of Physical Model and Machine Learning Method

Kankan Bai, Honghai Fan, Hongbao Zhang, Fei Zhou, Xingang Tao
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723045-MS
DOI: https://doi.org/10.15530/urtec-2022-3723045
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