Machine and deep learning for estimating the permeability of complex carbonate rock from X-ray micro-computed tomography

Abstract

Accurate estimation of permeability is critical for oil and gas reservoir development and management, as it controls production rate. After assessing numerical techniques ranging from pore network modeling (PNM) to the lattice Boltzmann method (LBM), an AI-based workflow is developed for a quick and accurate estimation of the permeability of a complex carbonate rock from its X-ray micro-computed tomography (micro-CT) image. Following features engineering using both image processing and PNM, we trained and tested the workflow on thousands of segmented 3D micro-CT images using both shallow and deep learning algorithms to assess the permeability. A broad variety of supervised learning algorithms are implemented and tested, including linear regression, support vector regression, improved gradient boosting, and convolutional neural networks. Additionally, we explored a hybrid physics-driven neural network that takes into account both the X-ray micro-CT images and petrophysical properties. Finally, we found that the predicted permeability of a complex carbonate by machine learning (ML) agrees very well with that of a more computationally-intensive voxel-based direct simulation. In addition, the ML model developed here provides a substantial reduction in computation time by roughly three orders of magnitude compared to that of the LBM. This paper highlights the crucial role played by features engineering in predicting petrophysical properties by machine and deep learning. The proposed framework, integrating diverse learning algorithms, rock imaging, and modeling, has the potential to quickly and accurately estimate petrophysical properties to aid in reservoir simulation and characterization.

Keywords

• Flow in porous media
• Digital rock physics
• Carbonate rock
• Machine learning
• Deep learning