An Experimental Study to Determine Optimal Injection Strategies for Water-Alternating-Solvent Process in Green and Brownfields
Bibliographic record
Abstract
Abstract Considering more than 50% of oil is left on average after primary and secondary methods in conventional reservoirs, techniques to recover the remaining oil from these proven and mature reserves is critically important to meet the future energy demand. Miscible solvent (usually gas) injection is performed in the form of water-alternating gas rather than continuous injection of expensive injectant. In this case, the process should be optimally designed for slug sizes and proper sequences of the solvent considering several controlling parameters including past history of waterflooding (water content) and wettability. We performed a set of experiments on vertically situated sand pack models saturated with 14cp oil. Heptane was selected as the solvent phase due to high miscibility conditions. Tests were started with waterflooding or solvent injection. Different slug sizes of solvent and water were tested on fully oil- saturated water, oil-wet samples, and samples with connate water (10 and 30%). The amounts of oil and solvent retrieved were monitored using refractometer. Using the collected data, the recovery rates and ultimate recoveries were comparatively analyzed. In addition to the technical feasibility, an economic analysis was performed considering the amount of solvent injected, oil and solvent recovered, and time for recovery. Starting the process with heptane was technically and economically feasible (pay-out time is shorter) in the short run for both the oil- and water-wet cases. This was true especially if the rock was oil-wet, which yielded faster recovery and higher ultimate recovery. Excessive water injection (up to the plateau level) preceding the solvent injection in the oil-wet case resulted in lower recovery factor whereas this design was very effective in the water-wet case. Therefore, the time to switch to solvent injection was critical in the oil-wet case and a short initial cycle of solvent injection followed by short waterflooding cycle is suggested. In the oil-wet case, initial waterflooding resulted in an inefficient process; while it yielded high ultimate recoveries, the process time was longer than other injection options. In the water- wet case, a greater amount of solvent was needed in the first cycle.
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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 | 0.000 |
| 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) | 0.001 | 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".