The principal aim of this investigation is to probe Artificial Intelligence (AI) techniques to foretell and regulate the complex phenomenon of simultaneous coning. Simulating the coning behavior is a costly and time-consuming method. As a result, a ready-made correlation that serves as a rapid estimate is necessary. This inquiry harnesses sophisticated AI models including Artificial neural network (ANN) to develop a robust empirical correlation for critical oil flow using the most efficacious parameters for managing coning. The study presents a 3D reservoir simulation model that has been tested for a variety of fracture characteristics, reservoir, and fluid properties including conductivity, permeability, density, permeability anisotropy, and viscosity. The newly proposed correlation is dimensionless, i.e., it applies to all vertically fractured wells in tight oil reservoirs, furnishing considerable benefits to the oil and gas sector.
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SEG/AAPG International Meeting for Applied Geoscience & Energy
August 27–September 1, 2023
Houston, Texas
Application of artificial intelligence for simultaneous water and gas coning problems in hydraulically fractured tight oil reservoir
Mihir Kumar
Mihir Kumar
Rajiv Gandhi Institute of Petroleum Technology
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Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, August 2023.
Paper Number:
SEG-2023-3906861
Published:
August 27 2023
Citation
Kumar, Mihir. "Application of artificial intelligence for simultaneous water and gas coning problems in hydraulically fractured tight oil reservoir." Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, August 2023. doi: https://doi.org/10.1190/image2023-3906861.1
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