Peace River Carmon Creek Project—Optimization of Cyclic Steam Stimulation Through Experimental Design
Bibliographic record
Abstract
Abstract Peace River Carmon Creek is a 100% Shell owned ultra-heavy oil lease located in north-western Alberta, Canada, approximately 700 km northwest of Edmonton (Fig. 1). It holds nearly eight billion barrels of 7°API oil in place, spread over 370 km2. The Carmon Creek Project targets possibly about half of that oil for development by cyclic steam stimulation (CSS). There are growth plans for a significant increase in oil production over the next five years. The purpose of this study was to optimize CSS well configuration and steaming strategy for each distinct reservoir area by deploying previously improved and history matched simulation models1. A full field static model was built, comprising over 400 wells. More detailed static sector models were also built for each distinct geological area and translated into elements of symmetry thermal simulation models. The choice of design parameters and handling of uncertainties were addressed in a phased manner. First, the smallest possible element of symmetry simulation model and the most efficient discrete fracture realization were determined. The next phase involved optimization of the well configuration and steaming strategy for each field area (based on approximate Net Present Value, NPV). The final phase entailed uncertainty analysis for the optimized design concepts and determining P15, P50, and P85 forecasts for each area. Experimental Design and Monte Carlo simulations were applied to further reduce the runs required for each phase. Although different optimum CSS designs were determined for each geological area, the modeling results can be generalized as follows: Horizontal well near the base of the reservoir is the optimum well type for CSS at Peace River. Well spacing less than 75 meters appears more attractive in the higher reservoir quality areas compared to the current assumption of 150 meters. In summary, a series of predictive CSS simulation models, primarily for horizontal wells, have been developed. Heavily aided by experimental design, a unique phased modeling workflow was applied to optimize well design and steaming strategy. Some of the suggested design components are already being tested at Peace River.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".