Optimization strategies and impact of low oil price on long term SAGD projects
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD) has been known as a commercially proven high ultimate recovery process for bitumen and heavy crudes. It is an energy intensive process, which is economical when oil price is above certain value. When the oil price goes below the economic threshold of project, steam injection can be decreased or completely stopped for a certain period of time, and can resume thereafter when the condition alters. The objective of this study is to provide comprehensive information about the effect of steam injection interruptions on thermal project performance. An optimization strategy for the SAGD process, in cases where steam injection interruption occurs, is discussed using actual reservoir models of different geological formations. An economical model is used to evaluate operating strategy effect on the net present value (NPV) of the project. The parameters, like shut‐in period, initial steam injection period, etc, are optimized for Athabasca type oil sand reservoirs. The results show several key mechanisms exist in the life cycle of the SAGD process that must be included to reflect the field scale behaviour; otherwise, the mechanistic simplicity of the models could lead to directional and semi‐quantitative conclusions. Among the mechanisms, temperature effect on basic petrophysical properties of reservoir rocks was found to have an important role in the oil recovery, and considerably impacts the results of optimization. When the steam injection is interrupted, an optimum shut‐in period can be determined to maximize the oil recovery. The optimum length of steam injection interruption depends on the initial steam injection period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".