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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".