A Novel Solvent Injection Technique for Enhanced Heavy Oil Recovery: Cyclic Production with Continuous Solvent Injection
Bibliographic record
Abstract
Abstract Solvent-based in-situ enhance oil recovery (EOR) techniques are extensively studied to improve oil recovery factors (RFs) for heavy oil reservoirs. Continuous injection, such as vapour extraction (VAPEX), and cyclic injection, such as cyclic solvent injection (CSI) are two main categories of solvent EOR techniques. The production rates of VAPEX are low in field tests. CSI does not show exciting results due to the quick reservoir pressure depletion and the sudden reservoir energy loss, which causes the oil to regain its viscosity. A new enhanced heavy oil recovery technique, cyclic production with continuous solvent injection (CPCSI), is proposed in this paper. In this process, a vapour solvent is continuously injected into the reservoir to maintain reservoir pressure and also supply extra gas drive to flood the diluted oil out through an injector that is located on the top of the reservoir, while a producer, which is located at the bottom of the reservoir, is operated in a shut-in/open cyclic way. A series of experiments have been conducted to validate the CPCSI performance by using different sand-pack models with 4¬-5 Darcy permeability, and saturated with Western Canada heavy oil sample. Gaseous propane is continuously injected through an injector at a constant pressure, which is below the dew point pressure, and producer was operated with a cycle of 50-minute-shut-in/10-minute-open period. Experimental results show that, the oil is diluted and drained down by gravity during shut-in period, then produced in producer opening period by solution gas drive and gas flush. In comparison with VAPEX and CSI, CPCSI offers free gas driving, and still keeps oil diluted in the well opening period and produced by the gas flush. The RFs are up to 85% of OOIP in 1-D tests. Also, shorter height model has highest average production rate in comparison with others. Test results are also validated by 2-D physical model, in which the RF is improved by 11% by using the lateral CPCSI compared with classic lateral VAPEX. Well configurations and the shut-in/open scenarios are key optimization factors that affect CPCSI performance. This work shows that CPCSI could be an alternative optimization production scenario for applying solvent based in-situ EOR techniques for Western Canada heavy oil reservoirs.
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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 | 0.001 | 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".