Cyclic Solvent Process Commercial Optimization
Bibliographic record
Abstract
Abstract Cyclic Solvent Process (CSP) is a non-thermal solvent-based heavy oil recovery technology that was invented and developed by Imperial Oil Resources Limited through a multi-year integrated research program. The commercial viability of potential development concepts and their associated uncertainties are also an active area of investigation. A key input to an economic model is the global (or development level) flow stream. The conventional approach of developing the global flow stream involves the determination of well schedule through a well prioritization algorithm that adheres to a set of flow stream capacity constraints. The resulting flow streams can then be passed to an economic tool to evaluate a set of KPIs (Key Performance Indicators) in an uncoupled manner. One of the main challenges encountered in this approach is that it is difficult to optimize the overall economic performance due to (1) the absence of well-defined objective function, (2) the decoupling of the flow stream generation and the economic calculations, (3) the pre-defined characteristics of the well prioritization algorithm. The main objective of this study is to develop a mathematical optimization model for CSP commercial projects. A two-stage optimization framework, which integrates Genetic Algorithm (GA) as master optimizer and Mixed Integer Linear Programming (MILP) as sub-optimizer, is described. A conceptual commercial scenario is simulated as a case study and economic uplift is demonstrated.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".