Optimization of Drainage-Area Configurations To Maximize Recovery From SAGD Operations
Bibliographic record
Abstract
Summary Placement of steam-assisted-gravity-drainage (SAGD) surface production pads and subsurface drainage areas in the McMurray formation to maximize the economic potential of an area is a challenging problem. The location of surface pads (SPs) and drainage areas has a large impact on their production performance because of several factors, including variation of bitumen in place, variation of the reservoir base surface, vertical conformance, areal conformance, interaction between different drainage areas and pads, and surface hazards. An optimization algorithm is presented to determine the positions and orientations of SPs and drainage areas over a reservoir area; therefore, the potential for economically recoverable bitumen is maximized. The optimization considers either a deterministic model of the relevant properties or multiple realizations to account for uncertainty. Optimization considers all drainage areas simultaneously to ensure joint optimality of an entire set. The algorithm is demonstrated using two realistic examples that show a significant improvement in potential recovery. The algorithm executes in a reasonable amount of computation time, considering the complexity of the problem.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".