Enhanced Cyclic Solvent Process (ECSP) for Heavy Oil and Bitumen Recovery in Thin Reservoirs
Bibliographic record
Abstract
Appropriate techniques have to be developed for improving heavy oil recovery from thin reservoirs in western Canada, where thermal methods suffer from heat loss to overburden/underburden and vapor extraction (VAPEX) is not effective because of the lack of efficient gravity drainage. Considering this, a hydrocarbon gas injection process in huff-n-puff mode, i.e., traditional hydrocarbon-based cyclic solvent process (CSP), has been tested to evaluate its applicability to such thin reservoirs. In the first part of this study, the behavior of methane huff-n-puff for heavy oil recovery is investigated by conducting a series of CSP cycles in a sandpack saturated with crude oil (with a viscosity of 1080.6 cP at 22 °C) and brine. The results of the six methane CSP cycles revealed that methane huff-n-puff is inefficient. The problem is that, during the production cycles, the reservoir pressure has to be greatly reduced to realize solvent gas drive. In doing this, the oil regains its high viscosity, because a large fraction of methane evolves out of the oil. To overcome this limitation and keep the oil viscosity low by maintaining most of the viscosity-reducing solvent in oil during the production period, we examined a new process, enhanced cyclic solvent process (ECSP). In ECSP, two types of hydrocarbon solvents are cyclically injected but in two separate slugs. One slug is more volatile (methane), and the other is more soluble (propane) in the heavy oil. A series of six ECSP cycles was conducted in the same sandpack used in the methane huff-n-puff tests. A total recovery of 34.30% original-oil-in-place (OOIP) was obtained through six ECSP cycles compared to 4.27% OOIP of six methane huff-n-puff cycles, indicating that ECSP improves the methane huff-n-puff for the heavy oil recovery in thin formations.
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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 | 0.000 |
| 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 | 0.000 |
| 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".