Water saturation modeling in carbonate reservoirs using the bulk volume water approach
Bibliographic record
Abstract
Abstract An ideal Saturation Height Function (SHF) should account for rock and fluid properties, as well as the Free Water Level (FWL), while ensuring consistency between measured and modeled data in a 3D reservoir model. This study employs the Bulk Volume Water (BVW) concept with the Bangestan and COSTA open-source carbonate datasets to develop a simple and practical SHF. A MATLAB Graphical User Interface (GUI) was designed to facilitate computations, including back-calculating capillary pressure (Pc) curves for comparison with input data and conducting error analysis. BVW is determined as the product of porosity and saturation at each Pc curve pressure step across a porosity range of 0.04 to 0.35. The relationship between BVW and pressure suggests that BVW is independent of porosity within the reservoir, allowing a single equation to represent all porosity variations. A power correlation with two constants was evaluated, and the correlation constants were derived from the BVW-pressure correlation. The results demonstrated strong agreement with the input data, and the generated capillary pressure curves closely matched the input Pc curves, confirming the reliability of the derived constants. An exponential correlation with four constants was tested on the same dataset, yielding a stronger correlation than the power function. The J-function was applied for comparison, but its predicted capillary pressure curves showed weaker correlation with the input data. Error analysis confirms that BVW-based SHFs offer a robust, permeability-independent alternative to the J-function, simplifying reservoir modeling by the need for rock type classification. The findings suggest that incorporating moderate- to high-porosity data improves the accuracy of estimated constants and enhances Pc curve synthesis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".