Forecasting Reservoir Water Losses in a SAGD Operation. A Combined Approach
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is the in-situ method of choice to recover bitumen from reservoirs in the Athabasca basin. In SAGD, steam is injected into an upper horizontal injection well, while emulsified bitumen and condensed water are produced from the lower horizontal production well. Produced water is treated and recycled to generate steam. Water management is a key factor in the operation of the whole process. Reservoir water losses are an essential part of the physics of the subsurface process with enormous implications for the water management. Prediction of reservoir water losses is critical to the design and operation of SAGD wells and facilities. For the purpose of forecasting water losses, three different approaches have been taken. The obvious inherent assumption in these methods is the ability of cold water to move through cold reservoir from higher pressure to lower pressure areas. This paper presents three methods to forecast reservoir water losses: the empirical, the analytical and the numerical simulation methods. These different approaches are complementary and incrementally complex allowing for flexibility (depending on time demand to create the forecast and required precision of the results). Ability to forecast water losses assists in planning the most efficient and reliable strategy to maximize future value of a SAGD operation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".