ROCK TYPE DETERMINATION OF A CARBONATE RESERVOIR USING VARIOUS APPROACHES: A CASE STUDY
Bibliographic record
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 that 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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".