Machine-Learning Image Recognition Enhances Rock Classification

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 196657, “Machine Learning for 3D Image Recognition To Determine Porosity and Lithology of Heterogeneous Carbonate Rock,” by Omar Al-Farisi, SPE, Hongtao Zhang, and Aikifa Raza, Khalifa University of Science and Technology, prepared for the 2019 SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, 17-19 September. The paper has not been peer reviewed. Automated image-processing algorithms can improve the quality and speed in classifying the morphology of heterogeneous carbonate rock. Several commercial products have produced petrophysical properties from 2D images and, to a lesser extent, from 3D images. Images are mainly microcomputed tomography (μCT), optical images of thin sections, or magnetic resonance images (MRI). However, most successful work is from homogeneous and clastic rocks. In the complete paper, the authors have demonstrated a machine-learning-assisted image-recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images from µCT and MRI. Introduction The authors’ literature review has revealed the pressing need to perform 3D image processing instead of 2D. Achieving this goal requires an interdisciplinary approach. This study deployed new analysis and verification approaches, including 3D micromodels (3DMM) with various micropore sizes and uses 3DMM as an image-processing calibration reference. Additionally, a new image-resolution enhancement for quality segmentation is developed. Porosity was determined mainly using two methods. The first is a standalone image processing, in which image-information extraction was successful. The second is MLIR. The difference between image processing and image analysis is important. If image processing enables extraction of meaningful data, then image analysis is the ability to interpret this data through numerical analysis.