Prediction Modeling for Combination Drive Reservoir Performance
Bibliographic record
Abstract
Depletion performance of combination drive oil reservoirs is highly influenced by changes in reservoir rock and fluid data, relative permeability data, and PVT data of reservoir. Therefore, future prediction of combination drive oil reservoirs is difficult due to the long terms, huge equations and the sensitivity of data especially the PVT data and relative permeability data. In this paper, an integrated analytical model was developed to simulate the combination drive oil reservoir’s performance. It couples the general material balance equation with equations for water influx, water-invaded pore volume, gas-invaded pore volume, oil and gas saturation, and fluid contacts for combination oil reservoirs. All these equations are merged and solved simultaneously with reservoir depletion stages. A comparison with the various equations’ results for the integrated model has been developed so that it can be utilized in history match mode. This is used to estimate fluid saturation distribution after water influx and gas-cap invasion, original fluids in place, aquifer parameters and type, fluid contact levels, and effective recovery factor during gas and water aquifer movement towards the productive hydrocarbon zone in all reservoir depletion stages. The developed model has been validated using published cases for various oil reservoirs’ conditions, resulting in a good match between published case results and developed model results for these reservoirs. After validating the model, it has been used for two Egyptian combination drive fields. The field production history has been matched and future production performance for these reservoirs was simulated. Finally, the developed model also has the capability to predict reservoir performance for another Egyptian combination drive oil reservoir field under water and or gas injection, integrated with decline curve analysis.
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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 | 0.000 | 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 | 0.001 |
| 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".