Pore Network Model Predictions of Darcy‐Scale Multiphase Flow Heterogeneity Validated by Experiments. Issue 6 (30th May 2020)

• Record Type: Journal Article
• Authors: Zahasky, Christopher; Jackson, Samuel J.; Lin, Qingyang; Krevor, Samuel
• Is Part Of: Water resources research. Volume 56:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-30
• Subjects: digital rock physics -- pore network model -- capillary heterogeneity -- X‐ray computed tomography -- multiphase flow -- simulation; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026708
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Small‐scale heterogeneities in multiphase flow properties fundamentally control the flow of fluids from very small to very large scales in geologic systems. Inability to characterize these heterogeneities often limits numerical model descriptions and predictions of multiphase flow across scales. In this study, we evaluate the ability of pore network models (PNMs) to characterize multiphase flow heterogeneity at the millimeter scale using X‐ray micro‐computed tomography images of centimeter‐scale rock cores. Specifically, PNM capillary pressure and relative permeability output are used to populate a Darcy‐scale numerical model of the rock cores. These pore‐network‐derived Darcy‐scale simulations lead to accurate predictions of core‐average relative permeability, and water saturation, as validated by independent experimental data sets from the same cores and robust uncertainty analysis. Results highlight that heterogeneity in capillary pressure characteristics is more important for predicting local and upscaled flow behavior than heterogeneity in permeability or relative permeability. The leading uncertainty in core‐average relative permeability is driven not by the image processing or PNM extraction but rather by ambiguity in capillary pressure boundary condition definition in the Darcy‐scale simulator. This workflow enables characterization of local capillary heterogeneity and core‐averaged multiphase flow properties while circumventing the need for the most complex experimental observations conventionally required to obtain these properties. Plain Language Summary: To understand how fluids flow in subsurface rocks, it is often necessary to perform laborious and expensive experiments aimed at replicating the subsurface pressure and temperature conditions. In this study, we propose and test a new modeling‐based approach using high‐resolution images capable of describing the structure and pore space of the rock at a resolution 10 times smaller than the width of a typical human hair. We show that with these high‐resolution images, along with a few routine rock property measurements, it is possible to predict the distribution of fluids in the rocks at range of subsurface fluid flow conditions. This digital, or experiment‐free, approach has the potential to redefine how we parameterize larger‐scale models of problems such as contaminant flow in aquifers or carbon dioxide migration and trapping in carbon capture and storage reservoirs. Key Points: Pore network models extracted from X‐ray micro‐computed tomography scans can predict capillary heterogeneity in subdomains of core samples Darcy‐scale simulation results, parameterized with pore network model output, agree well with independent experimental measurements A digital rocks approach is presented for multiphase characterization that requires no experimental calibration