A Machine Learning Workflow to Support the Identification of Subsurface Resource Analogs

Abstract

Identifying subsurface resource analogs from mature subsurface datasets is vital for developing new prospects due to often initial limited or absent information. Traditional methods for selecting these analogs, executed by domain experts, face challenges due to subsurface dataset's high complexity, noise, and dimensionality. This article aims to simplify this process by introducing an objective geostatistics-based machine learning workflow for analog selection. Our innovative workflow offers a systematic and unbiased solution, incorporating a new dissimilarity metric and scoring metrics, group consistency, and pairwise similarity scores. These elements effectively account for spatial and multivariate data relationships, measuring similarities within and between groups in reduced dimensional spaces. Our workflow begins with multidimensional scaling from inferential machine learning, utilizing our dissimilarity metric to obtain data representations in a reduced dimensional space. Following this, density-based spatial clustering of applications with noise identifies analog clusters and spatial analogs in the reduced space. Then, our scoring metrics assist in quantifying and identifying analogous data samples, while providing useful diagnostics for resource exploration. We demonstrate the efficacy of this workflow with wells from the Duvernay Formation and a test scenario incorporating various well types common in unconventional reservoirs, including infill, outlier, sparse, and centered wells. Through this application, we successfully identified and grouped analog clusters of test well samples based on geological properties and cumulative gas production, showcasing the potential of our proposed workflow for practical use in the field.