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Braden Bowie
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Proceedings Papers

Applying Numerical RTA to Public Data: Enabling Field-Wide Property Calibration and Improved Public Data EUR Forecasts

Braden Bowie, Jordan Bowie, Mathias Lia Carlsen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4029160-MS
https://doi.org/10.15530/urtec-2024-4029160
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Proceedings Papers

Numerical RTA in Tight Unconventionals

Mathias Lia Carlsen, Braden Bowie, Mohamad Majzoub Dahouk, Stian Mydland, Curtis Hays Whitson, Ilina Yusra
Paper presented at the SPE Annual Technical Conference and Exhibition, September 21–23, 2021
Paper Number: SPE-205884-MS
https://doi.org/10.2118/205884-MS
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Proceedings Papers

Numerically Enhanced RTA Workflow - Improving Estimation of Both Linear Flow Parameter And Hydrocarbons In Place

Braden Bowie, James Ewert
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2967-MS
https://doi.org/10.15530/urtec-2020-2967
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Proceedings Papers

Machine Learning Applied to Optimize Duvernay Well Performance

Braden Bowie
Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189823-MS
https://doi.org/10.2118/189823-MS
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Journal Articles

Machine Learning as a Reliable Technology for Evaluating Time/Rate Performance of Unconventional Wells

David S. Fulford, Braden Bowie, Michael E. Berry, Bo Bowen, Derrick W. Turk
Journal: SPE Economics & Management
Publisher: Society of Petroleum Engineers (SPE)
SPE Econ & Mgmt 8 (01): 23–39.
Paper Number: SPE-174784-PA
https://doi.org/10.2118/174784-PA
Published: 27 January 2016
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Proceedings Papers

Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

David S. Fulford, Braden Bowie, Michael E. Berry, Bo Bowen, Derrick W. Turk
Paper presented at the SPE Annual Technical Conference and Exhibition, September 28–30, 2015
Paper Number: SPE-174784-MS
https://doi.org/10.2118/174784-MS
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