Comparison of velocity prediction models for fully saturated carbonate rocks
Bibliographic record
Abstract
Despite the fact that Carbonate reservoirs contain more than half of the world's oil and gas reserves, their physical properties have not received enough attention. Carbonate rocks are formed from a combination of biological and chemical processes that add complexity and heterogeneity to its structure and petrophysical properties. Fluid saturation is one of the important factors that influences the elastic properties of rocks. Most studies on determining the applicability of using the Gassmann fluid substitution model in carbonates concluded that the Gassmann model, most of the time, is not suitable for predicting the observed saturated velocities in carbonates. This paper will compare alternative techniques for modeling these velocities using data from the Arab-D reservoir in Saudi Arabia. Here, water-saturated P- and S-wave velocities were measured in thirty seven carbonate samples from the Arab- D reservoir, and only a subset of fifteen of these were selected for the fluid model prediction study. The measured velocities were compared to model predictions from dispersion-free Gassmann, Biot, squirt-Gassmann and squirt-Biot models. Only eight of the selected samples were used in the squirt models, since these required the use of quasi-static data in order to calculate the compliant porosity as a function of confining pressure. We found that the squirt mechanism was not active on all the studied samples. For P-wave the Biot mechanism is likely to be the principle dispersion mechanism in these samples. For S-wave velocities, Gassmann's model consistently over-predictes the saturated velocities at low confining pressures, but closely fits those measured at high pressure, whereas the Biot model over-predicted the saturated velocities in most of the studied samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".