High-resolution core data and machine learning schemes applied to rock facies classification

Abstract

Abstract This paper presents core analysis practices developed to increase the value of cores in reservoir characterization workflows. Core analysis traditionally implies extensive rock testing programmes that require large numbers of plug samples. Numerous stakeholders compete for intact core material and often do not base sample selection criteria on objective and reliable information. As a consequence, samples dedicated to core analysis programmes consume a significant fraction of the material available, yet they are selected on the basis of very little a priori information and therefore do not necessarily capture the complexity of the targeted formations. In an attempt to change this paradigm, we promote a more integrated core analysis solution combining transdisciplinary, high-resolution, non-destructive measurements on whole cores, for an early yet objective description of cores and the rapid estimation of formation properties. The first section describes multidisciplinary and non-destructive tests designed to increase the value of information from cores while keeping a minimal footprint. Core samples are scanned along a fit-for-purpose surface, with a collection of sensors interfaced to the same table-top equipment. Technologies including ultra-high-resolution pictures, elemental composition and the direct measurements of geomechanical properties provide continuous and high-resolution profiles with a unique depth reference. Panoramic pictures are processed to extract textural and colour features and grain size distributions. Grain size distributions are backed up by analysis of topographical maps created with a laser scan. The results are combined under one unique format, thereby easing interdisciplinary work from the verification of standard tests (routine core analysis, rock mechanical test) to the construction of robust predictive rock models. The paper describes machine learning schemes applied to core datasets. Unsupervised schemes are designed to identify rock facies, while supervised schemes are used to classify tested rocks into predefined rock facies with known petrophysical properties. Case studies highlight the benefits of this approach of core analysis in conjunction with artificial intelligence for the automated identification and classification of rock facies. This novel approach to core analysis leverages a detailed and comprehensive knowledge of the distributions of core properties, available under one unique format for all disciplines, which eases interdisciplinary work and significantly improves existing core analysis standards. It also provides a sound basis to train artificial intelligence rock reservoir property predictors linking well-log data to core-based lithofacies signatures.