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

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 (mu xCT) 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 mu xCT 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.

Automated summary

This paper proposes a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores and build predictive continuum-scale models. The workflow classifies rock types using image features and morphological properties, and evaluates petrophysical properties through pore-scale simulations. Experimental results demonstrate the high-fidelity characterization provided by this workflow.