Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

Digital Rocks Portal (DRP, https://www.digitalrocksportal.org) organizes and preserves imaged datasets and experimental measurements of porous materials in subsurface, and beyond, with the mission to connect them to simulation and analysis, as well as educate the research community. We have over 150 projects represented in more than 200 publications, and an active community that reuses the data, most recently in multiple machine learning applications for automating image analysis as well as the prediction of transport. Such automation is crucial for performing formation evaluation tasks in near-real time. We present benchmark datasets that have played a role in recent machine learning prediction successes in the field. We further discuss the vision for further research advances, educational materials, as well as growth and sustainability plan of this digital rock physics community resource. In particular, we are in the process of expanding into a broader repository of engineered porous materials, specifically those for energy storage and the portal will transition to Digital Porous Media (DPM) in near future.

Imaging technology is constantly improving and enabling accurate, deterministic simulations of transport properties through the pore space of the imaged rock sample. Meanwhile, data-driven machine learning has emerged as an alternate tool for modeling transport properties that, once trained, use a fraction of the computational resources that traditional simulations require. However, machine learning models often fail to strictly enforce the physical constraints of the system, leading to solutions that are less accurate than that of traditional solvers. Here we propose a novel hybrid workflow that combines machine learning and conventional simulation methods. The workflow begins with a three-dimensional, binary image of a sample. A trained convolutional neural network extracts spatial relationships between the porous medium geometry and the electrostatic potential field and predicts the electrical properties through a new medium. Instead of assuming a linear potential gradient, this prediction is used as the initial condition of a validated finite difference solver. The implementation of this workflow can improve the simulation run time by an order of magnitude for small images. The success of the proposed workflow heavily depends on the accuracy of model prediction. We previously developed successful methods for prediction of the velocity field (and permeability) of a Newtonian fluid in a porous medium in the laminar regime. Here, we extend the method to predict the electrical potential field. We explore one strategy of improving a model's ability to generalize to unseen samples by supplying geometric characterizations of the pore space. We find that models trained with these features individually do not result in an improvement over the baseline model trained with only the binary image. However, they do provide the model with relational information that can be incorporated into future models. Analysis of electrical properties is one of the most common methods of delineating hydrocarbon saturation in reservoir rock. The proposed workflow helps accelerate the calculation of the electric potential field and can lead to estimating hydrocarbon saturation in real time. We also expect that this workflow is easily generalized to many other transport problems in porous media.

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