Automated lithofacies classification: A comprehensive machine learning approach in Shushan Basin reservoirs, Western Desert, Egypt

Abstract

The application of machine learning serves as a pivotal tool for petroleum geologists in facies classification. This new workflow distinguishes itself from existing classifiers by leveraging hidden statistical patterns in logging data to present a few recognizable clustering options for geologists. These choices are guided by other geological data sources, allowing geologists to retain the dimensional locations of chosen clusters for identification in other wells lacking these additional sources. The classification technique maximizes the value of conventional logging data (gamma ray, resistivity, density, neutron and sonic) for discerning rock typing, porosity ranking, fluid content, highlighting similar petrographic characteristics and elements composition, facilitating the inference of porosity and permeability degrees with high confidence.

The workflow is designed in this study to predict siltstone, shale, limestone, basaltic intrusions, and coal, accurately identifies various sandstone sub-facies, differentiates between tight and hydrocarbon-bearing sandstone across four wells, with blind validation on a separate well. The classification is validated using Litho Scanner tool, petrography thin sections, and laboratory analysis.

This comprehensive approach demonstrates the efficiency and applicability of the methodology, marking significant advancements in facies classification within petroleum geology.