Literature Review of Hybrid CO2 Low Salinity Water-Alternating-Gas Injection and Investigation on Hysteresis Effect
Bibliographic record
Abstract
Low salinity water injection (LSWI) is considered to be more cost-effective and has less environmental impacts over conventional chemical Enhanced Oil Recovery (EOR) methods. CO2 Water-Alternating-Gas (WAG) injection is also a leading EOR flooding process. The hybrid EOR method, CO2 low salinity (LS) WAG injection, which incorporates low salinity water into CO2 WAG injection, is potentially beneficial in terms of optimizing oil recovery and decreasing operational costs. Experimental and simulation studies reveal that CO2 LSWAG injection is influenced by CO2 solubility in brine, brine salinity and composition, rock composition, WAG parameters, and wettability. However, the mechanism for increased recovery using this hybrid method is still debatable and the conditions under which CO2 LSWAG injection is effective are still uncertain. Hence, a comprehensive review of the existing literature investigating LSWI and CO2 WAG injection, and laboratory and simulation studies of CO2 LSWAG injection is essential to understand current research progress, highlight knowledge gaps and identify future research directions. With the identified research gap, a core-scale simulation study on hysteresis effect in CO2 LSWAG injection is carried out. The results indicate different changing trend in oil recovery due to the impact of salinity on hysteresis and excluding of hysteresis effect in CO2 LSWAG injection simulation and optimization might lead to significant errors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".