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Proceedings Papers

Data-Driven Analysis for Causality of Parent–Child Interactions in the Bakken

Kyoung Suk Min, Alexander V. Chakhmakhchev, Xue Yu, Nicholas A. Azzolina, Darren D. Schmidt, Bethany A. Kurz, James A. Sorensen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862589-MS
https://doi.org/10.15530/urtec-2023-3862589
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View Paper titled, Data-Driven Analysis for Causality of Parent–Child Interactions in the Bakken
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Proceedings Papers

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
https://doi.org/10.15530/urtec-2022-3703284
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View Paper titled, Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
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Proceedings Papers

Completion Optimization in the Bakken Petroleum System Using Data Mining

Alexander Chakhmakhchev, Nicholas A. Azzolina, Bethany A. Kurz, Xue Yu, Chantsalmaa Dalkhaa, Justin T. Kovacevich, James A. Sorensen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723843-MS
https://doi.org/10.15530/urtec-2022-3723843
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View Paper titled, Completion Optimization in the Bakken Petroleum System Using Data Mining
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Proceedings Papers

Data to Decision: A Unified and Rapid Workflow for Unconventional Reservoirs Blending Data Analytics, Physics-Based Completion Optimization, and Investor- Oriented Economics

George Voneiff, Peter Bastian
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5454-MS
https://doi.org/10.15530/urtec-2021-5454
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View Paper titled, Data to Decision: A Unified and Rapid Workflow for Unconventional Reservoirs Blending Data Analytics, Physics-Based Completion Optimization, and Investor- Oriented Economics
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Proceedings Papers

A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells

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, July 26–28, 2021
Paper Number: URTEC-2021-5059-MS
https://doi.org/10.15530/urtec-2021-5059
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View Paper titled, A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells
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Proceedings Papers

Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs

Y. Kocoglu, M. E. Wigwe, G. Sheldon, C. Watson
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3108-MS
https://doi.org/10.15530/urtec-2020-3108
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View Paper titled, Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs
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Proceedings Papers

Spatio-temporal Models for Big Data and Applications on Unconventional Production Evaluation

M. E. Wigwe, E. S. Bougre, M. C. Watson, A. Giussani
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2855-MS
https://doi.org/10.15530/urtec-2020-2855
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View Paper titled, Spatio-temporal Models for Big Data and Applications on Unconventional Production Evaluation
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Proceedings Papers

Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale

Ali Rezaei, Fahd Siddiqui, Birol Dindoruk, M. Y. Soliman
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2751-MS
https://doi.org/10.15530/urtec-2020-2751
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View Paper titled, Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale
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Proceedings Papers

New Engineering and Geologic Parameters to Predict Infill Well Performance in the Wolfcamp of the Delaware Basin

S. Amin Gherabati, Katie M. Smye, Guin McDaid, Scott Hamlin
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3077-MS
https://doi.org/10.15530/urtec-2020-3077
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View Paper titled, New Engineering and Geologic Parameters to Predict Infill Well Performance in the Wolfcamp of the Delaware Basin
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Proceedings Papers

Permian Delaware Basin Wolfcamp A Formation Productivity Analysis and Technically Recoverable Resource Assessment

Yang Qian, Frank Male, Svetlana A. Ikonnikova, Katie Smye, Guin McDaid, Emery Goodman
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3167-MS
https://doi.org/10.15530/urtec-2020-3167
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View Paper titled, Permian Delaware Basin Wolfcamp A Formation Productivity Analysis and Technically Recoverable Resource Assessment
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Proceedings Papers

Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play

Luisa Porras, Christopher Hawkes, Arshad Islam
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2938-MS
https://doi.org/10.15530/urtec-2020-2938
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View Paper titled, Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play
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Proceedings Papers

Using Data Analytics to Maximize Value in the Denver-Julesburg Basin

Kristina Repchuk, Aaron Reimchen, Dane Gregoris
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2902938-MS
https://doi.org/10.15530/URTEC-2018-2902938
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View Paper titled, Using Data Analytics to Maximize Value in the Denver-Julesburg Basin
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Proceedings Papers

Spider Bots: Database Enhancing and Indexing Scripts to Efficiently Convert Raw Well Data Into Valuable Knowledge

Gurtej Saini, Hong-Chih Chan, Pradeepkumar Ashok, Eric van Oort, Michael Behounek, Taylor Thetford, Mojtaba Shahri
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2902181-MS
https://doi.org/10.15530/URTEC-2018-2902181
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View Paper titled, Spider Bots: Database Enhancing and Indexing Scripts to Efficiently Convert Raw Well Data Into Valuable Knowledge
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Proceedings Papers

Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity

Daria Khvostichenko, Sergey Makarychev-Mikhailov
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2903086-MS
https://doi.org/10.15530/URTEC-2018-2903086
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View Paper titled, Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity
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Proceedings Papers

Optimization of Completion and Well Spacing for Development of Multi-Stacked Reservoirs Using Integration of Data Analytics, Geomechanics and Reservoir Flow Modeling

Kyoung Suk Min, Vikram Sen, Lujun Ji, Richard B. Sullivan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2897656-MS
https://doi.org/10.15530/URTEC-2018-2897656
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View Paper titled, Optimization of Completion and Well Spacing for Development of Multi-Stacked Reservoirs Using Integration of Data Analytics, Geomechanics and Reservoir Flow Modeling
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Proceedings Papers

The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics

Kathryn Ball, Tristan Arbus, Uchenna Odi, Jessamyn Sneed
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2668073-MS
https://doi.org/10.15530/URTEC-2017-2668073
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View Paper titled, The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics
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Proceedings Papers

Predicting ESP Lifespan With Machine Learning

Jessamyn Sneed
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2669988-MS
https://doi.org/10.15530/URTEC-2017-2669988
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Proceedings Papers

Continental Production Allocation and Analysis Through Big Data

Beau T. Rollins, Andy Broussard, Billy Cummins, Akim Smiley, Nelson Dobbs
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2678296-MS
https://doi.org/10.15530/URTEC-2017-2678296
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Proceedings Papers

Using Machine Learning to Identify the Highest Wet Gas Producing Mix of Hydraulic Fracture Classes and Technology Improvements in the Marcellus Shale

Roger N. Anderson, Boyi Xie, Leon Wu, Arthur A. Kressner, Joseph H. Frantz, Jr., Matthew A. Ockree, Kenneth G. Brown, Peter Carragher, Mark A. McLane
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 1–3, 2016
Paper Number: URTEC-2430481-MS
https://doi.org/10.15530/URTEC-2016-2430481
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View Paper titled, Using Machine Learning to Identify the Highest Wet Gas Producing Mix of Hydraulic Fracture Classes and Technology Improvements in the Marcellus Shale
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Proceedings Papers

Regional Saltwater Disposal Facility Planning Utilizing Data Analytic Methods

Chad E. Kronkosky, Amin Ettehadtavakkol
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 1–3, 2016
Paper Number: URTEC-2460712-MS
https://doi.org/10.15530/URTEC-2016-2460712
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View Paper titled, Regional Saltwater Disposal Facility Planning Utilizing Data Analytic Methods
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