SAGD Production Optimization Through Preferential Steam Allocation
Bibliographic record
Abstract
Abstract Optimizing steam-assisted gravity drainage (SAGD) performance in oil sands reservoirs relies on the quality of steam allocation decisions made across the well inventory. With finite facility steam generation capacity, SAGD producers are typically challenged with identifying the true opportunity cost of allocating steam volumes based on well performance. This paper presents a novel technique to inform steam allocation decisions and managing SAGD reservoir pressures in service of optimizing production and consequently improving the economic performance of the asset through smarter SAGD field development planning. The concept of marginal steam-oil-ratio (mSOR) is introduced as a method of guiding steam allocation decisions. Marginal SOR is defined as the cold-water equivalent volume of steam required to produce the next marginal barrel of bitumen from the production system in a steam constrained environment. The metric represents the opportunity cost of deploying a barrel of steam to the next best alternative in steam allocation decisions. Dynamic quantification of mSOR over the plausible range of operating pressures for each producing entity (PRDE) in the inventory (such as a well group or drainage area) is critical to optimally allocating steam when faced with reservoir challenges such as reservoir complexity and heterogeneity and transient reservoir behaviors such as thief zone interaction. This paper prescribes methodologies to analytically and empirically quantify mSOR for a SAGD production system. Additionally, application of the concept if field production optimization is discussed under the context of integrated production modeling and constrained flow network optimization problems. A case example of applying mSOR to guide steam allocation decisions at ConocoPhillips' Surmont SAGD asset is presented under a steam constrained environment. The mSOR guided solution is validated using brute-force enumeration of steam allocation outcomes in the production system to prove production optimality. The results from this dynamic steam allocation strategy guided by mSOR characterization show significant improvements in field oil rates, field steam management efficiency and consequently the economic value of the SAGD asset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".