Quantifying Injection-Driven Mass Transfer within Porous Media via Time-Elapsed X-ray micro-Computed Tomography

TL;DR

This study compares three analytical frameworks for quantifying interphase mass transfer in porous media using time-lapse microCT data, introducing a volume-ratio filtering technique to improve accuracy.

Contribution

It evaluates the effectiveness of SAC, NPC, and CPC approaches across different conditions and proposes guidelines for selecting methods based on detail and computational resources.

Findings

All three approaches estimate mass transfer coefficients within one order of magnitude.

Estimates diverge when modeling complex phenomena like solute concentration profiles.

Higher resolution models require more computational resources and are more sensitive to noise.

Abstract

Understanding interphase mass transfer is essential for a variety of applications in porous media, ranging from groundwater remediation to geologic energy storage. While X-ray micro-Computed Tomography (microCT) provides critical in situ observations, analyzing mass transfer requires models and workflows compatible with the limited spatial and temporal resolution. Current literature presents three analytical frameworks for evaluating interphase mass transfer using microCT data: the Slice-Averaged Concentration (SAC) approach, the Non-Classified per-Cluster (NPC) approach, and the Classified per-Cluster (CPC) approach. This study evaluates the results of all three approaches across four sets of time-lapse tomography sequences that observe hydrogen dissolution at varying solvent injection rates. To mitigate biases arising from dissolution-driven cluster remobilization, we introduce a volume-ratio filtering technique to all workflows to ensure that estimates more accurately reflect true mass transfer events. Our analysis finds that all three analytical approaches estimate average mass transfer coefficients within one order of magnitude of one another at the same solvent injection rate. However, the similarity between the estimates of each approach diverges when approximating more complex phenomena, such as aqueous solute concentration profiles. Ultimately, the utility of one approach over another is determined by the desired level of system detail, at the cost of the computational resources required to achieve it. Higher phenomenological resolution requires greater computational processing and refinement due to increased sensitivity to measurement and processing noise, as well as outlier events. We anticipate that the findings will provide a framework for researchers to match analytical approaches to their available computational resources and desired level of physical detail.