Effects of Well Placement and Intelligent Completions on SAGD in a Full-Field Thermal-Numerical Model for Athabasca Oil Sands
Bibliographic record
Abstract
Abstract Estimated at 2.5 trillion barrels, Canada has the world's largest share of ultra-heavy oil and bitumen resources. While shallow heavy oil reserves are extracted from pit mines, deeper reserves can only be extracted through wells. Production of these reserves requires methods such as steam-assisted gravity drainage (SAGD) and cyclic steam simulation (CSS) (Butler, 1991). Optimal well placement defines the propagation of steam within the reservoir and the resulting flow of crude. SAGD recovery methods require tremendous amounts of steam in order to get the crude to flow. Costs to generate and inject steam in a SAGD pad are significant. Finding ways to use steam more effectively in these operations should result in increased production efficiency and improved financial return on these projects. By strategically placing steam injectors and by controlling the amount of steam injected it may be possible for these results to be achieved. A study was conducted to examine several completion strategies and to test, with the use of a simulation model, what the expected production, and steam requirements for these strategies would be. The simulation model examined multiple well pairs to see these results in a full-field environment. To assess the financial impact of these strategies, a fiscal model was developed that evaluated SAGD project costs and then examined the incremental cost and value of each of the completion strategies. Results show that although using steam effectively in these strategies may not yield the highest recovery, it does improve the expected value of these projects. This paper describes the process, operational control, and financial analysis used to design and validate the SAGD model. The study focuses on a completion strategy that demonstrates a strong potential to reduce SOR and ultimately operational cost. Incremental financial analysis is included to examine the impact of choosing any such strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".