Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec
Bibliographic record
Abstract
Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques and larger-scale transport properties. This study uses pre-existing medical CT-scan datasets of reference material, namely glass beads and conventional reservoir rocks (Berea sandstone, Boise sandstone, Indiana limestone) to extract the 3D geometry of connected pores using an open-source software (Spam). Pore networks from rock samples were generated from dry and then saturated samples. Binarized datasets were produced for these materials (generated by a thresholding technique) to obtain pore size distribution and tortuosity, as well as preferential paths for fluid flow. Average porosities were also calculated for comparison with those obtained by conventional commercial laboratory techniques. The results obtained show that this approach works well for medium and coarse-grained materials that do not contain a large percentage of fine particles. However, this approach does not allow representative networks to be obtained for fine-grained rocks, due to the fact that small pores (or pore throats) cannot be taken into account in the datasets obtained from the medical CT-Scan. A next step, using datasets produced from a micro- CT scan, is planned in order to be able to generate representative networks in this type of material as well.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".