Machine-Learning Techniques Characterize Source-Rock Images at the Pore Scale

The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods.

January 1, 2022

Journal of Petroleum Technology

Fig. 1—Image-based characterization and deep-learning work flow. Images are first acquired for a source-rock sample from one or multiple modalities. The image data then are curated, including alignment, normalization, and subsampling, to create a data set suitable for machine-learning model training. After training the image-processing model, new images are generated or predicted. The synthesized images are post-processed, including segmentation into pore and mineral phases, and the morphological and petrophysical properties of the sample evaluated.

Nanoimaging techniques for characterizing pore-scale structure of shales trade off between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Acquisition of nanoscale images also is often time-consuming and expensive. In the complete paper, the author introduces methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep-learning models.

Introduction

A promising application of data-driven scale-translation techniques is nanoscale imaging. This application is important for studying shales because of the importance of nanoporosity in shale gas storage.

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