Integrated Optimization of Hybrid Steam-Solvent Injection in Post-CHOPS Reservoirs with Consideration of Wormhole Networks and Foamy Oil Behavior
Bibliographic record
Abstract
Summary For this paper, integrated techniques have been developed to optimize the performance of the hybrid steam-solvent injection processes in a depleted post-cold heavy oil production with sand (CHOPS) reservoir with consideration of wormhole networks and foamy oil behavior. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined using the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to maximize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. The genetic algorithm (GA) has been integrated with orthogonal array (OA) and Tabu search to maximize the NPV by delaying the displacement front as well as extending the reservoir life under various strategies. Considering the wormhole network and foamy oil behavior and using the NPV as the objective function, such a modified algorithm can be used to allocate and optimize the production-injection strategies of each huff ‘n’ puff (HnP) cycle in a post-CHOPS reservoir with altered porosity and increased permeability within a unified, consistent, and efficient framework.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".