Solvent-aided Steam-flooding Strategy Optimization in Thin Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Stochastic optimization based on a simulated annealing method were carried out to determine the optimum steam and steam-solvent flooding strategies in thin (4 m) heavy oil reservoir both in the absence and presence of a bottom water zone. The steam injection pressure optimization case determined a technically feasible operating strategy. However, the cumulative energy to produced oil ratio (cEOR) realized from the optimized process is high. In comparison, the solvent-aided steam optimization case achieved an operating stratety that obtains a much lower cEOR and cumulative water-to-oil ratio (cWOR) than those in the optimized injection pressure-only strategy. We observed that a solvent channel forms at the top of the reservoir after breakthrough of solvent to the production well. The formation of the solvent channel led to oil-solvent mixing at the periphery of the channel as well as heat transfer to oil beyond the channel, which leads to better recovery performance. In the the presence of a bottom water zone, the optimized steam injection pressure optimization strategy was found to perform poorly. However, the optimized solvent-aided strategy achieved superior economics. With solvent injection, the presence of the bottom water zone enhanced mixing of solvent and oil yielding better oil recovery performance. Background In Western Canada, about 80% of heavy oil resources are found in reservoirs less than 5 m thick (Adams 1982). Although currently commercial thermal-based techniques such as Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) are highly successful for recovering bitumen and heavy oil from thick pay zone (> 15 m), their application in thin heavy oil (<6 m) reservoirs are generally not thought to be economically viable. This is due to the high steam-to-oil ratio (SOR) which caused by significant heat losses to the overburden relative the amount of heat delivered to the oil which renders the processes uneconomic. Cold production (CP) employs small energy input. However, the average recovery factor is typically low, usually, between 3 to 8% of the Original Oil In Place (OOIP) (Adams 1982). By employing so called Cold Heavy Oil Production with Sand (CHOPS) technique, the recovery factor can reach as high as 15% (Pan et al. 2010). However, the formation of wormholes during CHOPS operation creates new challenges for applying follow-up processes to recover additional oil beyond CHOPS.
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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) | 0.001 | 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".