Sensitivity Analysis of the Effect of Pore Structure and Geometry on Petrophysical and Electrical Properties of Tight Media: Random Network Modeling
Bibliographic record
Abstract
Several methodologies published in the literature can be used to construct realistic pore networks for simple rocks, whereas in complex pore geometry formations, as formed in tight reservoirs, such a construction still remains a challenge. A basic understanding of pore structure and topology is essential to overcome the challenges associated with the pore scale modeling of tight porous media. A stochastic random generation algorithm was employed to assess the effects of certain pore structure and geometries on the estimation of petrophysical and electrical properties of tight media through physically realistic 3D random networks. A Weibull truncated equation was used to predict the distribution of network pores and throats. An equivalent 3D pore network of Berea Sandstone was generated based on published pore and throat size distributions. The estimated porosity, absolute permeability, and formation factor of the reconstructed pore network are in good agreement with published laboratory measurements. Moreover, the estimated drainage and imbibition relative permeability curves are in a good match with corresponding experimental relative permeability curves. Subsequently, the effect of pore structure on basic core properties is evaluated by varying the Berea network pore size, throat size, and coordination number (connectivity) distributions. Finally, the effect of pore and throat geometries on two phase flow properties is investigated. The study shows the importance of taking into consideration the internal pore structure for petrophysical and electrical properties estimation.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".