Fast Resistivity Logs Simulation in Two-Dimensional Anisotropic Near-Wellbore Space Models Based on Numerical Simulation and Machine Learning

Abstract

The article presents the results of a new approach application for oil well galvanic and induction resistivity logs simulation to enhance the efficiency of geological environment parameters evaluation and to speed up the interpretation. The use of modern machine learning technologies allows us to create algorithms for resistivity logs simulation in high-detailed two-dimensional anisotropic geoelectric models. The developed algorithms are characterized by a qualitatively new level of performance compared to the approaches used today.