RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Timothy I. Anderson, Kelly M. Guan, Bolivia Vega, Saman A. Aryana and Anthony R. Kovscek
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Timothy I. Anderson: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
Kelly M. Guan: Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA
Bolivia Vega: Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA
Saman A. Aryana: Department of Chemical Engineering, University of Wyoming, Laramie, WY 82071, USA
Anthony R. Kovscek: Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA

Energies, 2020, vol. 13, issue 24, 1-19

Abstract: Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

Keywords: porous media; image analysis; shale; deep learning; generative flow model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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