Numerical Sensitivity Analysis and Simulation of Petrophysical Characteristics of Porous Media Domains
Bibliographic record
Abstract
The interaction between capillary, gravity and viscous forces in porous media results in complex multi-phase fluid flow phenomena affecting pore-scale fluid occupancies during drainage and imbibition. The reconstruction and characterization of digital rock physics in such sophisticated systems is certainly non-trivial, as there is a myriad of technical constraints ranging from numerical convergence issues over fluid interfaces to limited hardware capacity and computational resources. The accurate and interactive visualization of high-resolution pore-scale data-sets is also a challenge as it requires high-end, high-powered graphics workstations. Although there are some advanced and efficient techniques proposed to simulate pore-scale fluid flow displacements, it is still a complicated task to successfully initialize a simulation case and choose the right computational domain, boundary conditions, solvers and assumptions. This work starts with single-phase simulations of electric current and fluid flow in an extensive dataset of synthetic microscale domains in order to investigate the hydraulic-electric analogy in granular porous media. The excellent practicality and reliability of pore-level computational fluid dynamic simulations are demonstrated, and two sets of models for the determination of electric and hydraulic tortuosities in unconsolidated packs of thousands of spherical grains are proposed and validated. The second thesis objective is to conduct two-phase pore-level simulations, together with a sensitivity analysis, in order to determine the range of applicability of some simplifying assumptions regarding the forces that control the physics of flow and the arrangement of fluid-fluid and fluid-solid interfaces. We show that the pore space spatial geometry and rock wettability are the primary parameters that dictate the amount of residual trapping. Lastly, a workflow is proposed to set a maximum allowable level of image upscaling for high-resolution large data sets of different rock types, thus allowing to minimize the number of computational nodes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".