Direct Pore-Level Examination of Hydraulic Electric Analogy in Porous Media
Bibliographic record
Abstract
Abstract Taking advantage of the analogy between hydraulic and electrical flows to facilitate the prediction of porous media characteristics is a longstanding practice in petroleum engineering. The relationship between hydraulic and electrical properties is widely used in well-logs interpretation and characterization of transport properties of porous media relying on the strong correlation between electric and hydraulic flow conductance. However, due to the lack of direct investigations, the similarity among their pathways/tortuosities is still unclear. It is a challenging and almost impractical task to identify the streamlines experimentally. Here a series of direct finite element numerical simulations are conducted within pore-level microstructures to extract and compare the streamlines of both electric and fluid flow currents and examine the accuracy of the analogy by predicting the petrophysical characteristics of the case studies. The fluid flow and electric transports are simulated through pore-level digital rocks of synthetic unconsolidated sand packs representing the Athabasca oil sands deposit as the second largest oil reserve in the world. The formation factor, porosity, and absolute permeability of the media under consideration are predicted, and consequently, the streamlines of both electric and hydraulic currents are extracted and compared in terms of length, shape, and pathways. According to the results, the fluid flow pathways pose differently and are longer than the homogeneous electric current streamlines. The ratio between the hydraulic and electric tortuosities follows a polynomial trendline, and a local extremum occurs at the porosity of eighteen percent. Pedotransfer functions for tortuosities, dimensionless permeability, and formation factor are proposed underpinning the rigorous relationships between transport processes in rocks.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".