Performance Evaluation of Hybrid Steam-Solvent Processes in a Post-CHOPS Reservoir With Consideration of Wormhole Network
Bibliographic record
Abstract
Abstract In this paper, techniques have been developed to evaluate performance of steam, solvents, and hybrid steam-solvent processes in a post-CHOPS reservoir with consideration of wormhole network. With the experimentally determined properties of injected gases and reservoir fluids, history matching is accomplished for the reservoir geological model conditioned to the fluid and sand production profiles together with pressure. Meanwhile, the wormhole network is characterized with the pressure gradient-based (PGB) sand failure criterion. Once the history matching is finished, the calibrated reservoir geological model can be used to quantify the contributions of steam, solvents, and hybrid steam-solvent processes under various conditions. The results show that huff-n-puff processes have a very good performance on oil production and recovery when wormhole network is fully generated and propagated. Among all the solvent-based methods, a pure CO2 huff-n-puff process has been proven to be more efficient than flue gas, while the addition of alkane solvents is also beneficial to oil recovery compared with CO2 only method. Since the addition of C3H8 and n-C4H10 will significantly decrease the heavy oil viscosity and enhance the swelling factor, all hybrid steam-solvent injection achieves high oil recovery by taking the advantage of both hot steam and solvents injection.
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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 | 0.000 | 0.001 |
| 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".