Integrated Optimization of Hybrid Steam-Solvent Processes in a Post-CHOPS Reservoir With Consideration of Wormhole Networks
Bibliographic record
Abstract
Abstract In this paper, an integrated technique has been developed to evaluate and optimize performance of hybrid steam-solvent processes in a post-CHOPS reservoir with consideration of wormhole networks. A reservoir geological model is developed and calibrated by history matching reservoir pressure with the oil, gas, and water production rates as the input constraints, while its wormhole network is characterized with a newly developed pressure-gradient-based (PGB) sand failure criterion. Once calibrated, the reservoir geological model incorporated with the wormhole network is then employed to evaluate and optimize performance of hybrid steam-solvent processes under various conditions, during which the net present value (NPV) is maximized with an integrated optimization algorithm by taking injection time, soaking time, production time, and injected fluid composition as controlling variables. It is found that a huff-n-puff process imposes a positive impact on enhancing oil recovery when wormhole network is fully generated and propagated. Among all solvent-based methods, a pure CO2 huff-n-puff process shows a better performance than flue gas, while the addition of alkane solvents leads to a higher oil recovery compared with that of the CO2 only method. Since the addition of C3H8 and n-C4H10 will significantly decrease heavy oil viscosity and enhance oil swelling, all hybrid steam-solvent injection achieves high oil recovery by taking advantage of both thermal energy and solvent dissolution. It is found that the NPV reaches its maximum of C$5.36 × 107 when the steam temperature is 200°C for the optimized hybrid steam-solvent scenario.
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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) | 0.001 | 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".