RESERVOIR ROCK TYPE ANALYSIS USING STATISTICAL PORE SIZE DISTRIBUTION
Bibliographic record
Abstract
Reservoir rock type determination is one of the main parameters that plays a governing role in the simulation and prediction of hydrocarbon reservoir behavior. Hence, it is of great importance to use a method that is capable of determining the rock type accurately. In the present study, a statistical pore size distribution model was used to analyze a reservoir rock type. A parametric probability distribution function was proposed to determine the pore size distribution. Then, the mathematical capillary pressure and J -function models were generated based on this proposed probability function. The results of the mathematical capillary pressure were well matched to the experimental data of the capillary pressure. Therefore, the parameters that are associated with the distribution function were estimated by fitting the capillary model to the measured capillary pressure for each rock sample. As a consequence, the estimated parameters were used to specify a unique pore size distribution function for each rock sample. Finally, the obtained pore size distribution functions classified the rock samples into three discrete rock types that have similar distribution curves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".