IMPROVEMENT OF PERMEABILITY MODELS USING LARGE MERCURY INJECTION CAPILLARY PRESSURE DATASET FOR MIDDLE EAST CARBONATE RESERVOIRS
Bibliographic record
Abstract
In this study, eight permeability models are calibrated to a large mercury injection capillary pressure dataset obtained from the Middle East region. The permeability models are: Purcell, Thomeer, Winland, Swanson, Pittman, Huet, Dastidar, in addition to the Buiting-Clerke permeability models. The coefficients of the models have been determined using three different regression techniques: ordinary nonlinear least-squares regression, weighted nonlinear regression, and multiple regressions of nonlinear models after linearization. Using the original and adjusted coefficients, permeability values were estimated and compared to the actual data. Comprehensive statistical and graphical comparison is made between the different regression techniques. The study indicates that, in general, permeability models with published constants produce high errors. Major improvements in results, however, have been accomplished when using the generalized permeability models with their calibrated coefficients. The modified Winland and Swanson models show the best prediction performance. In addition, the modified Purcell model shows a significant improvement with the updated parameters. This study enhances the estimation of absolute permeability and hence better reservoir description.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".