Using Nano-XRM and High-Contrast Imaging to Inform Micro-Porosity Permeability During Stokes–Brinkman Single and Two-Phase Flow Simulations on Micro-CT Images
Bibliographic record
Abstract
Carbonate rocks have multiscale pore systems that are weakly understood. In this study, we use combined experimental, modeling, and pore space generation methods to tackle the impact of microporosity on the flow properties of Estaillades limestone. First, a nano-core from a microporous grain of Estaillades limestone was scanned using nanotomography (nano-XRM). The information from the nano-XRM scan was then used as input into an object-based pore network generator, on which permeability fields were simulated for a range of porosities, creating a synthetic Kozeny–Carman porosity–permeability relationship targeted for the specific microporous system present in Estaillades. We found a good match between the experimental and simulated Mercury Intrusion Capillary Pressure (MICP) range in the imaged geometry and a good match between the imaged and object-generated permeabilities and MICP. A micro-core of Estaillades was then scanned using X-ray microtomography (μCT), the differential pressure was measured during single-phase flow, and the rock was flooded with doped brine. The contrast between the images was used to assign a porosity to each voxel of connected microporosity. The flow through the pore space was solved using the Stokes–Brinkman (S–B) and Stokes-only solvers, and the differences between the measured permeability and computed permeabilities were evaluated. An agreement was seen between the computed permeability of the Stokes and S–B simulation with the measured permeability. However, the velocity fields with the S–B simulation captured stagnant regions of the pore space that were not present in the Stokes simulations. Additionally, we investigated the implications of including microporosity in the estimation of relative permeability. Nitrogen was experimentally co-injected through the core with doped brine at a 50% fractional flow and imaged to capture the two-phase effective permeability and was compared with the simulated numerical permeability. The Stokes simulation was not able to predict relative permeability with this method due to the major flow paths in the macroporosity being impeded by the injected non-wetting phase. The S–B simulations, however, allowed flow in the microporous regions around these blocked flow paths and were able to achieve a relative permeability prediction that was a reasonable match to the experimental measurement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".