Comparing the Performance and Recovery Mechanisms for Steam Flooding in Heavy and Light Oil Reservoirs
Bibliographic record
Abstract
Abstract The concern over fossil energy shortage for the next decade leads to the extensive research activities in the area of enhanced oil recovery. Steam injection as one of well known EOR process has been used for about five decades to improve the oil production rate and recovery efficiency. Steam flooding is applied to heavy and extra-heavy oil reservoirs; however it could be used in light oil reservoirs in which water injection do not work effectively. Regardless of different performances, this method is an efficient EOR process for both heavy and light oil reservoirs. In this work, two separate numerical models were prepared to investigate steam flooding performance for the recovery of light and heavy oil. The heavy oil model is a Cartesian hypothesis model with properties of Cold Lake heavy oil reservoir in Canada and light oil model is a sector of an Iranian fractured light oil reservoir. For this purpose, steam flooding was implemented in these two models separately. Then according to software options, all possible recovery mechanisms (viscosity reduction, steam distillation, thermal oil expansion and others) were simulated individually to measure the effectiveness of each recovery mechanism in total recovery of heavy and light oil during steam flooding. Also, operational parameters such as steam quality, steam flow rate and well perforation were optimized for both reservoirs. Results show that steam flooding performances in heavy and light oil reservoirs are different. Heavy oil reservoirs do not response fast to steam compared to the light oil reservoirs. Furthermore, viscosity reduction is a main recovery mechanism in recovery of heavy oil and contribute to 80% of total recovery, while in recovery of light oil all three main recovery mechanisms have the same contribution to total recovery. It was also found that the optimized operational parameters are different for each reservoir.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".