Reservoir characterization

Machine-Learning Image Recognition Enhances Rock Classification

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.

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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.

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