Design of field‐scale cyclic solvent injection processes for post‐CHOPS applications
Bibliographic record
Abstract
Abstract Oil recovery factors in cold heavy oil production with sand (CHOPS) are typically lower than 15 %. Solvent‐aided processes, such as cyclic solvent injection (CSI) are common post‐CHOPS approaches, where wormhole networks could offer increased reservoir contact. However, grid block sizes in field‐scale simulations are much larger than the wormhole scale and large‐scale dispersivity values are assigned arbitrarily based on history matching. This work implements a statistical scale‐up workflow that facilitates the construction of coarse‐scale models for CSI simulation, whose relevant parameters are calibrated against simulation results using high‐resolution wormhole networks. The formulated workflow can be integrated with commercial reservoir simulators to effectively simulate solvent processes at multiple scales. Multiple injection scenarios are analyzed. Extended soaking periods may positively impact the ultimate recovery with a slower decline at later times, while a lower initial rate is observed. Interestingly, when an economic limit is imposed, the optimal soaking time is not necessarily the longest one. It depends on the trade‐off between extracting additional oil recovery at late times versus producing at a higher rate at early times. The analysis also reveals that the initial cycles contribute the most to the final recovery. In addition, when the amount of solvent available is limited, the results would support the strategy of injecting all the solvent in 1 single cycle, with an extended soaking period, rather than performing shorter consecutive cycles.
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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 | 0.001 | 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".