An Empirical Oil, Steam, and Produced-Water Forecasting Model for Steam-Assisted Gravity Drainage With Linear Steam-Chamber Geometry
Bibliographic record
Abstract
Summary Presented here is a steam–assisted–gravity–drainage (SAGD) forecasting technique for oil and water production and steam injection based on linear steam–chamber geometry and an empirical form for the progression of the steam/oil interface with time. The oil forecast model generates full–cycle SAGD production profiles using three empirical inputs (the initial plateau oil rate qo,si, an exponent n, and the rising–phase–chamber angle θ) and volumetric parameters (well length, pay thickness, porosity, half–well spacing, and initial and residual oil saturation). Steam and produced–water forecasts are derived analytically using the resulting steam–chamber geometry and the heat required for the fluids, rock, and overburden. Peak rate is a direct input in this methodology, and as a result only volumetric and some thermodynamic parameters are required, but not fluid or transmissibility inputs, such as viscosity and permeability. This allows for direct use of commercial SAGD production data in the forecasting process. The model predictions are validated at a high level with field data from Devon Canada Corporation's Jackfish SAGD project and numerical simulation.
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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) | 0.001 | 0.000 |
| 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".