Nabiye Cold Lake Expansion - Leveraging Technology to Create Success
Bibliographic record
Abstract
Abstract The Cold Lake development, located in Alberta, Canada, is the world’s largest heavy oil in situ thermal development. At Cold Lake, operated by Imperial Oil Resources, an ExxonMobil affiliate, the Cyclic Steam Stimulation (CSS) process is used to produce 23,500 m3/d (150 kB/d) of heavy oil. In 2009, Cold Lake produced its one billionth barrel (160 million m3) of heavy oil. The Nabiye project will be the fifth central steam generation and fluid processing hub added at Cold Lake. Nabiye (Dené for Otter) continues the historical Cold Lake development concept of maximizing value through the utilization of a phased development strategy. Relative to current operations, the key reservoir difference at Nabiye is reduced pay thickness. Averaging 12 meters (40 feet), Nabiye pay is about half as thick as the initial pads of the previous expansion (Mahkeses). While reservoir of similar thickness as Nabiye is currently being developed as Productivity Maintenance pads to sustain production in the existing operation, the risk profile for Nabiye is higher because new plant investment is required. As Cold Lake develops more challenging subsurface environments, more advanced reservoir engineering techniques must be employed to mitigate risk. This paper describes the extensive use of both thermal simulation and wellbore integrity modeling to complement analog performance prediction techniques. This paper will demonstrate how the Nabiye project is effectively commercializing an unconventional resource by integrating analog performance data and advanced reservoir and geomechanical modeling. The application of (1) thermal simulation for performance prediction and (2) geomechanical modeling for steam strategy optimization will be presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".