Estimating Klinkenberg-Corrected Permeability From Mercury-Injection Capillary Pressure Data: A New Semianalytical Model for Tight Gas Sands
Bibliographic record
Abstract
Abstract This paper presents the practical applications of a semi-analytical model for estimating the Klinkenberg-corrected permeability from mercury-injection capillary pressure (Hg-pc) data in tight gas sands. The fundamental relationships between rock pore size/geometry and basic rock properties are well-documented in the petroleum literature. Moreover, since rock pore characteristics can be accurately quantified from interpretation of mercury-injection capillary pressure data, the literature is replete with models for estimating permeability from Hg-pc data. However, existing Hg-pc models tend to yield inconsistent results — and few models have been shown to be directly applicable for low-permeability sands. The basis of our model is the Purcell/Burdine model (bundle of capillary tubes) combined with the Brooks/Corey model (power law relationship of capillary pressure versus wetting phase saturation). We tested our model using more than 100 sets of mercury-injection capillary pressure data. Effective porosity in our data set ranges from 4 to 14 percent, while absolute permeability ranges from 0.005 to 0.5 md. The primary technical contribution of this paper is a tuned model for estimating Klinkenberg-corrected permeability from mercury-injection capillary pressure (Hg-pc) data in tight gas sands. The final form of our model allows estimation of the absolute (Klinkenberg-corrected) permeability as a function of effective porosity, irreducible wetting phase saturation, displacement pressure, and pore size characteristics. The model is also reversible — we can estimate a capillary pressure profile from routine permeability and porosity data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".