Optimal Amount of Solvent in Solvent Aided Process
Bibliographic record
Abstract
Abstract The Solvent Aided Process (SAP), described previously in literature, is an improvement to SAGD that promises to enhance the economics of bitumen/heavy oil recovery projects and reduce their impact on the environment. In SAP, a small amount of hydrocarbon solvent (such as a low molecular weight alkane) is introduced as an additive to the injected steam during SAGD. The viscosity of the oil thus is reduced due to solvent dilution in addition to heating. SAP can significantly improve the energy efficiency of SAGD, thus reducing the heat requirement. Cenovus's field trials of SAP, discussed elsewhere, have shown the practical upside of this process. Modeling predicts that the higher the amount of solvent used in SAP, the better is the performance (rates, energy intensity) of the recovery process. Besides rate of Bitumen production, economics of SAP depend on the availability and cost of solvent. Although the existing literature has discussed deterministic variations in solvent input, it is largely silent on how much solvent is the right amount of solvent in SAP. This paper contains discussion of using optimal amount of solvent with steam in SAP. The discussion is based on modeling and compares performance of the scheme under various solvent injection strategies. It explores effect of temporal variation in the concentration of solvent at the vapor-liquid interface as well as of pulsed solvent injection on the performance of the process.
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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".