Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models
Shwetank Krishna,
Sayed Ameenuddin Irfan,
Sahar Keshavarz
et al.
Abstract:Predicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model … Show more
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