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Proceedings Papers

Machine Learning Applications for Reservoir Pressure Trend Prediction in Potash Area of Delaware Basin

Olabode Thomas Ajibola, James Sheng, Phillip McElroy, Ebru Unal
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
https://doi.org/10.15530/urtec-2022-3722944
... decision making but also lead to safe and improved concurrent recovery of oil and gas and potash resources in the area. machine learning artificial intelligence log analysis fuzzy logic upstream oil & gas node pore pressure reservoir characterization us government eaton accuracy...
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Proceedings Papers

Mapping the Natural Fracture Network in Utica Shale Using Artificial Intelligence (AI)

Shahab D. Mohaghegh
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2669739-MS
https://doi.org/10.15530/URTEC-2017-2669739
.... hydraulic fracturing fuzzy logic lateral length shale play shale well urtec 2669739 frac job complex reservoir category Well Productivity productivity fuzzy membership value natural fracture network completion shale Utica Shale NFN Artificial Intelligence Upstream Oil & Gas...
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