Logging Data-Driven Geomechanical Parameter Estimation Using Advanced Machine Learning Techniques

Abstract

The most commonly used methods of conventional geomechanical parameters estimation which rely on costly, sparse laboratory tests and empirical correlations based on just a few well logs are linked to uncertainties and spatial gaps. This study reveals an innovative data-driven model, which incorporates Advanced Machine Learning techniques to precisely and efficiently estimate key geomechanical properties based directly on collected well-logging data. The techniques include, Deep Learning Architecture (DL), Deep Neural Network (DNN) and Artificial Neural Network (ANN). The machine learning application ensures a huge boost to yielding high prediction accuracies and that of running continuous and high-resolution profiles of geomechanical properties along the wellbore. This method is fast, and has low-cost geomechanical characterization that is vital to optimal drilling, hydraulic fracturing design, reservoir management, and subsurface integrity assessment, resulting in improved operating safety and efficiency. The estimated geomechanical parameters include elastic properties (young’s modulus and poisson’s ratio) and rock’s strength (unconfined compressive stress), while the artificial neutral network technique was applied to estimate the geomechanical parameters in the oil wells of Akata, Agbada and Benin Formations in Bonny Island, Rivers State.