Carbonate dry rock modeling for time-lapse seismic integration with reservoir simulation models
Bibliographic record
Abstract
Integrating time-lapse data with reservoir simulation models is crucial for effective reservoir management and monitoring. 4D seismic aids model updates via history matching or data assimilation, while reservoir models enhance 4D seismic interpretation and feasibility studies. This integration often requires petro-elastic modeling. However, modeling dry rock properties (bulk and shear modulus) presents particular challenges in carbonate reservoirs due to their complex pore system. This study compares dry rock properties modeling approaches using a commonly used inclusion model versus a simplified data-driven proxy model. Both approaches yield satisfactory calibration with well-log data, and in this study, we demonstrate the impact of incorporating them into 3D/4D modeling using numerical reservoir models. The analysis is based on an ensemble of simulation models from the carbonate Tupi field focusing on Barra Velha Formation (BVE).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".