Integrated Optimization of Hybrid Steam-Solvent Injection in Post-CHOPS Reservoirs with Consideration of Wormhole Networks and Foamy Oil Flow
Bibliographic record
Abstract
Abstract In this paper, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. With the experimentally determined properties of injected gases and reservoir fluids by performing PVT tests, history matching of the reservoir geological model is completed through the relationship between fluid and sand production profiles and reservoir pressure. Meanwhile, the wormhole network has been inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Once the history matching is completed, the calibrated reservoir geological model is used to optimize the solvent(s) and CO2 concentrations, provided that thermal energy, injection rates, and flowing bottomhole pressures are chosen as the controlling variables. The genetic algorithm has been modified and used to maximize the objective function of net present value (NPV) while delaying the displacement front as well as extending the reservoir life with optimal oil recovery under various strategies. Depending on the formation pressure and temperature, soaking time is optimized as a function of solvent concentration and fluid properties. Subsequently, considering the wormhole network and foamy oil flow, such a modified algorithm can be used to allocate and optimize the production-injection strategies with the NPV as the objective function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".