Solvent-Selection Criteria Based on Diffusion Rate and Mixing Quality for Steam/Solvent Applications in Heavy-Oil and Bitumen Recovery
Bibliographic record
Abstract
Summary Heavy-oil and bitumen recovery requires high recovery factors to offset the extreme high cost of investments and operations. Attention has been given to solvent injection for this purpose, and it has been observed that high recoveries are achievable when it is combined with steam injection. Heavier (“liquid”) solvents (liquid at ambient conditions) are especially becoming more popular because of availability and transportation. High oil prices will allow the application of this kind of technique if a proper design is made to retrieve the injected solvent efficiently. “Liquid” solvents are advantageous because they yield a better-quality mixing (especially with very heavy oils and bitumen) but a lower diffusion rate than lighter solvents such as propane or butane. Despite this understanding, there still is not a clear screening criterion for solvent selection to mitigate both diffusion rate and the quality of the mixture. In this study, two main solvent-selection-criteria parameters—diffusion rate and mixing quality—were considered to evaluate solvent-injection efficiency at different temperatures. An optical method under static conditions and image-processing techniques were proposed to determine 1D diffusivity of liquid solvent into a wide range of oil samples in a capillary tube. This sampling range varies from 40-cp oil to 250-cp oil, for which digital-image treatment was developed. X-ray computerized tomography (CT) was applied for heavier (and darker) oils (viscosity range of 20,000 cp to 400,000 cp). The diffusion coefficients were then computed through nonlinear curve fitting on the basis of an optimization algorithm to ensure that the obtained values were in agreement with available analytical solutions. Next, viscosity measurements and asphaltene precipitation for the same heavy-oil/solvent mixtures were performed to determine the mixing quality. The ideal solvent types for different oil types were determined by using the results from the diffusion-rate and mixing-quality experiments. The experimental and semianalytical outcome of this research would be useful in the determination of the best solvent type for a given oil and in understanding the key factors that influence the quality of mixtures including viscosity reduction and probable asphaltene precipitation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".