Viscous Oil Recovery and In Situ Deasphalting in Fractured Reservoirs: Part 1. The Effect of Solvent Injection Rate
Bibliographic record
Abstract
Steam-based EOR methods for viscous oil recovery from fractured reservoirs have significant challenges in both cost and energy efficiency. In response, solvent-based methods have been of interest because of their low energy intensity, low greenhouse gas emissions, and no fresh water consumption. Injection strategies for viscous oil recovery by solvent include liquid extraction and vapor oil gravity drainage. Understanding the mechanisms in each phase is of great value for the successful application and optimization of solvent EOR processes. The work presented here studies the effect of solvent injection rate on viscous oil recovery by liquid extraction with n -butane in vertically placed sandstone cores with an artificial fracture. The oil production rate, ultimate recovery, and in situ deasphalting in different sections of the core are analyzed. The oil production rate increased with solvent injection rate until it leveled off as the injection rate exceeded a critical value. The ultimate recovery factor is nearly the same for all solvent injection rates below the critical value. However, it is significantly reduced at higher injection rates. A conceptual model based on convective mass transfer is proposed and the effect of mechanical dispersion is discussed. In situ deasphalting was observed in all cases. The cause of the unexpected changes in production rate was attributed to severe asphaltene deposition and remobilization in the fractured permeable rock. In such a medium, solvent injection rate seems to show an optimal value for maximizing oil production rate, ultimate recovery factor, and solvent efficiency.
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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 | 0.000 | 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 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".