Integrated Economic Model for Evaluation and Optimization of Cyclic Steam Stimulation Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The development of any hydrocarbon resource should be planned to maximize the net present value (NPV) of the asset to stakeholders, subject to any imposed constraints. For example, in the evaluation of a single oil or gas well on primary production, assuming no additional constraints, maximization of the NPV may be obtained by maximizing recoverable volume, production rate, and realized product price, while at the same time minimizing capital and operating costs, royalties, and taxes. Maximization of the NPV of a thermal heavy oil project is significantly more involved than that of a single oil or gas well on primary production. This is due to the complex interplay of individual well production and injection profiles with field level production and injection constraints imposed by the 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 paper presents the results of a study to maximize the NPV of a greenfield CSS project by incorporating a recently developed horizontal CSS analytical model with a new surface model and economic evaluation model developed specifically for this purpose. The integration of the sub-surface, surface, and economic models allows for the optimization of input parameters simultaneously across the models to maximize the NPV of the entire project. The overall workflow and resulting optimized case will be summarized and discussed. In addition, stochastic simulation concepts are applied to the model to produce a distribution of results based on various input parameters. Stochastic simulations are already used in unconventional gas evaluations, and the authors believe that they will become an important tool to assist in the evaluation of thermal heavy oil projects due to the significant upfront capital cost and uncertainties associated with such developments.
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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 | 0.002 | 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 | 0.000 |
| 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 it