Pore pressure prediction based on conventional well logs and seismic data using an advanced machine learning approach

Abstract

Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties, ensures safe and efficient drilling operations, and optimizes reservoir characterization and production. The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs. This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset, particularly in complex formations. The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships. It integrates the best Gradient Boosting Regressor (GBR) algorithm to model pore pressure at wells and later utilizes Continuous Wavelet Transformation (CWT) of the seismic dataset for spatial analysis, and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume. In the second stage, for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area, a three-dimensional pore pressure prediction is conducted using CWT. The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure. Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction, the GBR has provided exceptional results that have been validated by evaluation metrics based on the R2 score i.e., 0.91 between the calibrated and predicted pore pressure. Via the deep neural network, the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.