Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Abstract

The influence of core‐scale heterogeneity on continuum‐scale flow and laboratory measurements are not well understood. To address this issue, we propose a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores for predictive continuum‐scale models. While the proposed AI‐based workflow inherently has no trained knowledge of rock petrophysical properties, our results demonstrate that image features and morphological properties provide sufficient measures for petrophysical classification. Micro X‐ray computed tomography (μxCT) image features were extracted from full core plug images by using a Convolutional Neural Network and Minkowski functional measurements. The features were then classified into specific classes using Principal Component Analysis followed by K‐means clustering. Next, the petrophysical properties of each class were evaluated using pore‐scale simulations to substantiate that unique classes were identified. The μxCT image was then up‐scaled to a continuum‐scale grid based on the defined classes. Last, simulation results were evaluated against real‐time flooding data monitored by Positron Emission Tomography. Both homogeneous sandstone and heterogeneous carbonate were tested. Simulation and experimental saturation profiles compared well, demonstrating that the workflow provided high‐fidelity characterization. Overall, we provided a novel workflow to build digital rock models in a fully automated way to better understand the impacts of heterogeneity on flow.

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