Integrated Economic Model for Evaluation and Optimization of Cyclic-Steam-Stimulation Projects
Bibliographic record
Abstract
Introduction The development of a hydrocarbon resource should be planned to maximize the net present value (NPV) of the project, subject to any imposed constraints. Maximizing the NPV of a thermal heavy-oil project can be complex because of the interplay of individual-well production and injection profiles with field-level production and injection constraints imposed by a central processing facility (CPF). In addition, for thermal heavy-oil-recovery methods such as cyclic-steam stimulation (CSS), the scheduling of the production, soak, and injection cycles of the wells has a significant impact on the overall project NPV. This study presents the results of a novel study to maximize the NPV of a greenfield CSS project by incorporating a newly developed analytical horizontal CSS model coupled to a field-production aggregation and scheduling model, which was in turn coupled to an economic-evaluation model. The close integration of these models allowed for the optimization of input parameters to be achieved simultaneously across all three models to maximize the NPV of the entire project. The integrated model work flow and the resulting optimized case will be summarized and discussed in detail. The significance of the work flow developed in this study is that it demonstrates that the key design parameters (such as the CPF capacity and schedule) of a thermal heavy-oil-exploitation scheme can be calculated and optimized on the basis of the economics of the entire project by use of an integrated model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".