In this study, an artificial neural networks (ANN) model as an artificial intelligence (AI) technique is proposed to determine the formation pore pressure from data of two critical drilling parameters named mechanical specific energy and drilling efficiency. These parameters (MSE and DE) which are closely correlated to differential pressure during drilling were chosen as a result of a literature review of proposed methods of pore pressure estimation. Collected data of a three wellbores drilled in an Iranian sandstone formation were used for the purpose of this research, and pore pressure estimated using this model was in a good agreement with estimates from previously published models including the one derived from conventional sonic logs data. The proposed model results were analyzed, and proved that artificial neural networks are capable to provide reliable independent predictions of pore pressure, and this smart model can be hired to analyze data for pre-drilling prediction models construction and post-well prediction models optimization.
An Artificial Intelligence Approach in Estimation of Formation Pore Pressure by Critical Drilling Data
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Rashidi, M., and A. Asadi. "An Artificial Intelligence Approach in Estimation of Formation Pore Pressure by Critical Drilling Data." Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, Seattle, Washington, June 2018.
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