Statistical Evaluation of Reservoir Rock Type in a Carbonate Reservoir
Bibliographic record
Abstract
Abstract Reservoir rock type determination is one of the main parameters for simulation and prediction of the hydrocarbon reservoir behavior. Hence, it is of great importance to use a method which is capable of determining the rock type accurately. In this study, some of the most useful methods such as capillary pressure, Leverett dimensionless J-function, Winland R35 method, Flow Zone Indicator (FZI) and Discrete Rock Type (DRT) were applied to samples from a carbonate reservoir to determine the various reservoir rock types. The sample set consisted of 265 routine core data and 18 data sets of capillary pressure versus initial water saturation; all were analyzed using the aforementioned determination methods. Results of this study showed that both capillary pressure and Winland R35 were not accurate enough to determine rock types for this carbonate reservoir, mainly because of the high heterogeneity in the reservoir rock properties. For the same reason, the Leverett J-function method was found to be problematic in normalizing all the capillary data into one unique curve. However, FZI and DRT methods successfully classified all data into four discrete rock types while satisfying the relationships between permeability and porosity for each of them. The calculated permeability data for each rock type classified by FZI and DRT methods were in good agreement with core permeability 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".