Can Solvent Injection be an Option for Cost Effective Enhanced Oil Recovery?: An Experimental Analysis for Different Oil and Rock Characteristics
Bibliographic record
Abstract
Abstract We performed a set of experiments on vertically situated sandpack models. Different slug sizes of water and solvent (heptane used in the experiments) were tested for 2,000 cp heavy-oil. As a benchmark, tests were also performed for 14 cp light oil for comparative analysis. In addition to the technical feasibility, an economic analysis was performed considering the amount of solvent injected and oil and solvent recovered. Experiments were repeated for oil-wet systems. For both light and heavy oils, starting the process with the solvent was feasible in the short run technically and economically. If the process starts with water, excess amount of it occupies the largest pores and hinders solvent-oil interaction for mixing and oil displacement. This was true especially if the rock is both oil-wet and heavy, which yielded faster recovery and higher ultimate recovery than the water-wet case. The time for switching to solvent injection is more critical in the heavy-oil case as it is more sensitive to the amount of existing water in the system. As oil becomes heavier and if the rock is water-wet, starting the process with waterflooding is not suggested. In this case, more solvent needs to be injected in the first cycle compared to oil-wet systems. Due to partial miscibility and more gravity stable nature, solvent retrieval and sweep with water can be more effective in case of heavy-oil compared to light oil (fully miscible case) and, as a result, can be even more profitable. This is highly critical in exploitation of heavy-oil reservoirs if thermal options are limited and greenhouse gas emission is a concern.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".