An evaluation of enhanced oil recovery strategies for a heavy oil reservoir after cold production with sand
Bibliographic record
Abstract
Cold heavy oil production with sand (CHOPS) is the process of choice for unconsolidated heavy oil reservoirs with relatively high gas content. The key challenge of CHOPS is that the recovery factor tends to be between 5% and 15%, implying that the majority of the oil remains in the ground after the process is rendered uneconomic. Continued cold production (without sands) is not productive for a post-CHOPS reservoir because of the low oil saturation and depleted reservoir pressure in the wormhole regions. There is a need to develop viable recovery processes for post-CHOPS reservoirs. Here, different follow-up processes are examined for a post-CHOPS heavy oil reservoir. In post-CHOPS cold water flooding, severe water channeling is ineffective at displacing high viscosity heavy oil. Hot water flooding improves the sweep efficiency and produces more oil compared with cold water flooding. However, the swept region is limited to the domain between the neighboring wormhole networks, and the energy efficiency of the process is relatively poor. Compared with the hot water flooding case, steam flooding achieves higher oil production rates and lower water use. A cyclic steam stimulation strategy achieves the best performance regarding oil production rates and water usage. Based on our results, it is observed that thermally based techniques alone are not capable to recover the oil economically for post-CHOPS reservoirs. However, it is suggested that techniques with combined use of thermal energy and solvent could potentially yield efficient oil recovery methods for these reservoirs. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".