A Heuristic Production Model for Cyclic Steam Stimulation in a Fractured Heavy Oil Carbonate Reservoir
Bibliographic record
Abstract
Abstract The Upper Devonian Grosmont formation is considered to be Canada's next largest unconventional oil resource play, with an estimated 406 billion barrels of heavy oil in place. A number of production pilots targeting the Grosmont formation have tested various thermal enhanced oil recovery techniques, which include steam flood, combustion, and cyclic steam stimulation (CSS). To date, the CSS process has demonstrated considerable promise based on performance from the Unocal Buffalo Creek Phase 2 pilot and through the application of C-SAGD, a CSS variant designed for the Grosmont, at the Laricina-Osum joint-venture pilot in Saleski. While the methodologies for forecasting CSS production profiles are well understood for clastic oil sands reservoirs, no direct analogue exists for carbonate reservoirs such as the Grosmont. The current profiling model for clastics appears to be suitable for matching and forecasting the production characteristics of CSS and the early cycles of C-SAGD in the Grosmont. However, a few extensions to the current profiling models are required to address differences in cycle length, bitumen ramp up, and oil cut characteristics. For instance, oil cuts are typically low initially and increase with time for the majority of cycles in clastic oil sands, whereas in the Grosmont, there is observed cycle-to-cycle variability. The first few cycles typically show high initial oil cuts, decreasing with time; in subsequent cycles, the oil cuts behave similarly to clastic reservoirs. These differences can be attributed to the presence of secondary porosity and permeability systems in the carbonate formation, such as vugs and fracture networks, and their interactions with the matrix. This paper will describe the modification to existing models for profiling production, based on field observations from the aforementioned pilots.
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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".