Stochastic Optimization of Hot Water Flooding Strategy in Thin Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Stochastic optimization, based on a simulated annealing method, was done to determine the optimum hot water-flooding strategy for recovery of oil from thin (<6 m) heavy oil reservoirs. The results reveal that high injection pressures are critical to a successful hot water flooding strategy. For water temperature during injection, the results show from a thermal efficienty point of view that that it is most efficient to adopt a temperature profile where the injection temperature starts high and ends at low water temperature. Multiple cycles of this profile might be beneficial depending on the reservoir conditions. The lower temperature injection at later stages of the recovery process partially recovers the heat stored in the reservoir matrix and therefore increases the overall heat utilization efficiency. A sensitivity analysis shows that the permeability distribution affects the performance of the hot water flooding process most significantly. The existence of a higher permeability zone in the lower part of the reservoir leads to earlier oil production and water breakthrough. The absolute permeability value has the largest effect on process performance. High permeability was found to lead to more oil and water production in the early stage of operation and achieved the best economic performance. The low permeability case was found to show a slow oil production. Although it has the lowest cumulative injected energy to oil produced ratio, poor oil production made the operation process uneconomic. Keywords: thin heavy oil reservoirs, hot water-flooding, energy to oil ratio, stochastic optimization, simulated annealing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".