IMPROVEMENT OF PERMEABILITY MODELS USING LARGE MERCURY INJECTION CAPILLARY PRESSURE DATASET FOR MIDDLE EAST CARBONATE RESERVOIRS
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it