Solvent Content Measurements of the Produced Stable Water-in-Oil Emulsions During Solvent-Aided Thermal Recovery Processes
Bibliographic record
Abstract
The in situ solvent-aided thermal recovery processes are promising methods for recovering unconventional oil resources due to their higher efficiency, lower energy and water consumption, and reduced environmental impacts. The produced stream is often a highly stable water-in-oil emulsion, where the oil phase comprises bitumen and solvent. Separating water from such a sample is challenging because conventional approaches result in solvent loss and sample contamination and render the solvent content measurement techniques currently employed by the industry invalid. Developing analytical techniques for solvent detection without sample dehydration, solvent loss, and contamination is essential for production surveillance, monitoring, process optimization, and economic evaluation. Solvent recovery and concentration measurement in produced streams have been considered the most important issues associated with the success and commercialization of solvent-assisted recovery processes. In this work, we implement a comprehensive chromatographic technique to measure the solvent concentration of the actual bitumen/solvent/water emulsions produced during a large 3D physical model experiment of the solvent-aided recovery process. We used combined gel permeation chromatography (GPC) and gas chromatography (GC) to obtain the full characterization of bitumen/solvent/water systems. After characterizing four produced emulsion samples, actual and synthetic multicomponent solvents are used to establish the necessary calibrations for rapid and accurate determination of the organic solvent content in the produced emulsion samples. The results demonstrated that the automated GC/GPC can be applied to actual minute amount emulsion samples for fast detection of solvent content in the pilot and field-scale projects of solvent-aided thermal recovery processes without sample dehydration while solvent loss and sample contamination are entirely avoided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".