Numerical modeling of effective elastic properties for heterogeneous porous media. Application to a case study of a carbonate reservoir rock sample
Bibliographic record
Abstract
An application of an open-access software destined for fast calculation of the contribution of an individual inhomogeneity of one of two types (cracks or inclusions/pores) to calculation of the effective elastic properties of an oil reservoir rock sample extracted from a wellcore fragment containing isolated pores is presented. We obtained the pore geometries by a 3D image from an X-ray tomography processed and converted into stereolithography (.stl) format. The data required for the calculations besides the shapes of the inhomogeneities are the Young’s modulus and the Poisson’s ratio of the matrix (in the isotropic case); these data were found by geological analysis. After we had calculated the contribution of each pore to the elastic properties of the rock sample, we obtained the overall effective elastic properties by the Mori–Tanaka scheme. The proposed methodology is straightforward and it was possible to detect even a slight anisotropy (less than 5%) of the effective elastic properties. We found experimentally the effective elastic properties of the sample from the measurement of the acoustic wave velocities. The results obtained show a good agreement in terms of anisotropy and porosity detection; however, the effective elastic properties diverged by a large margin (up to 200%). This may be explained by the presence of microcracks undetected by the tomography. As a future work, we consider a more thorough study of the microstructure of the sample to verify the hypothesis about the presence of microcracks.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".