An Ethane-Based CSI Process and Two Types of Flooding Process as a Hybrid Method for Enhancing Heavy Oil Recovery
Bibliographic record
Abstract
Combining multiple secondary oil recovery (SOR)/enhanced oil recovery (EOR) methods can be an effective way to maximize oil recovery from heavy oil reservoirs; however, previous studies typically focus on single methods. In order to optimize the combined process of ethane-based cyclic solvent injection (CSI) and water/nanoparticle-solution flooding, a comprehensive understanding of the impact of injection pressure, water, and nanoparticles on CSI performance is crucial. This study aims to provide such understanding through experimental evaluation, advancing the knowledge of EOR methods for heavy oil recovery. Three approaches (an ethane-based CSI process, water flooding, and nanoparticle-solution flooding) were applied through a cylindrical sandpack model with a length of 95.0 cm and a diameter of 3.8 cm. Test 1 conducted an ethane-based CSI process only. Test 2 conducted a combination approach of CSI–water flooding–CSI–nanoparticle-solution flooding–CSI. Specifically, the injection pressure of the first CSI phase in Test 2 was gradually increased from 3500 to 5500 kPa. The second and the third CSI phases had the same injection pressure as Test 1 at 5500 kPa. The CSI process ceased once the oil recovery was less than 0.5% of the original oil in place (OOIP) in a single cycle. Results show that the ethane-based CSI process is sensitive to injection pressure. A high injection pressure is crucial for optimal oil recovery. The first CSI phase in Test 2, where the injection pressure was increased gradually, resulted in a 2.9% lower oil recovery and five times as much ethane consumption compared to Test 1, which applied a high injection pressure. It was also found that water flooding improved the oil recovery in the CSI process. In Test 2, the oil recovery factor of the second CSI phase increased by 57% after the water flooding process, which is likely due to the formation of water channels and a dispersed oil phase that increased the contact area between ethane and oil. Although the nanoparticle-solution flooding only had 0.3% oil recovery, after that the third CSI phase stimulated another 10.8% of OOIP even when the water saturation achieved more than 65%. This demonstrated that the addition of nanoparticles can maintain the stability of the foam and enhance the transfer of ethane to the heavy oil. Finally, Test 2 reached a total oil recovery factor of 76.1% on a lab scale, an increase of 45% compared to the single EOR method, which proved the combination process is an efficient method to develop a heavy oil field.
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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 | 0.001 |
| 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".