CO2 Low Salinity Water Alternating Gas: A New Promising Approach for Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract It has been recognized that there are significant advantages on combining low salinity waterflooding (LSW) with other enhanced oil recovery (EOR) techniques such as polymer or low tension surfactant flooding. This paper proposes a novel concept of low salinity water-alternating-CO2 (CO2 LSWAG) injection under CO2 miscible displacement conditions. While LSW is an emerging EOR method based on alteration of wettability from oil-wet to water-wet conditions, WAG is a proven method for improving gas flooding performance by controlling the gas mobility. Therefore, LSWAG injection promotes the synergy of the mechanisms underlying these methods (i.e., ion-exchange, wettability alteration, and CO2 miscible displacement and mobility control) that further enhances oil recovery and overcomes the late production problem frequently encountered in the conventional WAG. These features are demonstrated in this work based on a field case study. To investigate the advantages of CO2 LSWAG, a comprehensive ion exchange model associated with geochemical processes has been developed and coupled to the multi-phase multi-component flow equations in an equation-of-state compositional simulator. Laboratory core flood simulations of different CO2 LSWAG schemes are conducted to understand the combined effects of solubility of CO2 in brine, dissolution of carbonate minerals, ion exchange, and wettability alteration. CO2 LSWAG performance is then evaluated on a field scale through an innovative workflow that includes geological modeling, multi-phase multi component reservoir flow modeling and process optimization. The simulation results indicate that CO2 LSWAG has the highest oil recovery compared to conventional high salinity waterflood, high salinity WAG, and low salinity waterflood. A number of geological realizations are generated to assess the geological uncertainty effect, in particular clay distribution uncertainties, on CO2 LSWAG efficiency. Finally, CO2 LSWAG injection strategies are optimized by identifying key WAG parameters. The proposed workflow demonstrates the synergy between CO2 WAG and LSW. Built in a robust reservoir simulator, it serves as a powerful tool for screening, design, optimization, and uncertainty assessment of the process performance from laboratory to and field scales. CO2 LSWAG is a promising EOR technique as it not only combines the benefits of CO2 injection and low salinity water floods but also promotes the synergy between these processes through the interactions between geochemical reactions associated with CO2 injection, ion exchange process, and wettability alteration. This paper demonstrates the merits of this process through modeling, optimization and uncertainty assessment.
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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".