Measurement of Recovered Solvent in Solvent Aided Process
Bibliographic record
Abstract
Abstract Solvent Aided Process (SAP) is a solvent based enhancement of SAGD where small amounts of solvent such as light alkanes or natural gas liquids are added to the injected steam to enhance reservoir performance and associated project economics. Expectedly, the economics with SAP are sensitive to the solvent recovered from the reservoir, making its measurement in a field test an important factor. When a single-component solvent like butane, which is not generally present in the produced heavy oil or bitumen, is used in SAP, estimation of the recovered solvent can be achieved uniquely. But when the solvent also has heavier components, some of which overlap with the lighter components of the produced oil, the measurement is not straightforward. The problem is compounded by the fact that the interaction with reservoir changes composition of the produced solvent and makes it time-varying on account of different resident times associated with different components. The issue is further complicated by the fact that produced oil also undergoes an in situ solvent de-asphalting process (SDA), which is also time and space varying in the reservoir. If there were no in situ SDA, one potential method to measure the amount of produced solvent would be to measure the total asphaltene content as an oil ‘marker’ in the produced blend. Use of a tracer with injected solvent as well as regression based analyses for solvent-fraction (using compositional analyses of solvent, bitumen and the blend) of the produced blend is error-prone for these same reasons. Due to the issues in the approaches mentioned above, a new method is desirable for a more robust and unique assessment of the solvent amount in the produced fluids. This paper elaborates on the current challenges and proposes a couple of workable methods including use of maltenes-metals content as oil markers as well as the use of boiling point curves of the produced blend compared to that of the base oil. Such techniques of estimating the recovered solvent can facilitate a more objective assessment of SAP field tests and enable economic evaluation of its application to a commercial scale.
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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) | 0.002 | 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".