Improving Cold Heavy Oil Development at Peace River with the Understanding of Foamy-Oil Dynamics
Bibliographic record
Abstract
Abstract Cold heavy oil production (CHOP) exploits the mechanism of enhanced solution gas (or foamy-oil) drive to achieve an economic oil rate and ultimate recovery. Understanding the foamy-oil dynamics and being able to simulate the process enable us to evaluate the cold production potential more realistically. This has led to the improved cold heavy oil recovery in Shell Canada's Cliffdale development in Peace River area. A recently developed foamy-oil dynamic model has been employed to evaluate the effects of well spacing and patterns on oil recovery. The results revealed that under the foamy-oil drive, the oil recovery improves as the well spacing decreases, because of decreasing well spacing leads to faster reservoir pressure depletion and stronger foamy-oil drive. In contrast, with a conventional black-oil model, the estimated ultimate oil recovery stays constant irrespective of the well spacing, and the only benefits of down spacing would be the production acceleration. The benefits of capturing foamy oil dynamics for the evaluation of CHOP development has been demonstrated with an example of a high level economic screening approach for the search of optimal well spacing and the number of laterals. The evolution of typical well spacing and the number of well laterals with time in the Peace River CHOP development has resulted from both the ever-increasing field operation experience and the improved understanding of foamy-oil drive dynamics.
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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 | 0.001 | 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".