Novel CO2-Soluble-Surfactant-Based Foam System for EOR And Geological CO2 Storage in High-Water-Cut Reservoirs
Bibliographic record
Abstract
Abstract This study introduces an innovative method using supercritical CO2 as a carrier for soluble surfactants to enable in-situ foam generation within reservoir formations. The technique enhances oil recovery and CO2 storage while improving economic feasibility and environmental sustainability in high-water-cut reservoirs. The molecular design of an environmentally friendly CO2-soluble surfactant is achieved through structure-performance relationships. This architecture strategically integrates multi-methyl branched alkyl chains with polypropylene oxide (PPO) and polyethylene oxide (PEO) units. The surfactant’s solubility in CO2 is systematically evaluated under high-temperature and high-pressure conditions using cloud point measurements. Foam stability assessments are conducted in high-salinity environments through half-life tests. Comparative sandpack flooding experiments quantify oil recovery and CO2 sequestration efficiency against CO2 flooding and conventional foam techniques. The CO2-philic enhancement is predominantly attributed to PPO groups. Multi-methyl branching reduces hydrocarbon chain hydrophobicity. PEO groups reinforce foam stability in high-salinity reservoirs through hydrogen-bond networks. The surfactant exhibits a solubility of 0.3 wt.% in CO2 at 20 MPa and 50°C, with a foam half-life exceeding 30 min in brine with salinity up to 85,255 ppm. In-situ foam generation preferentially occurs in high-water-saturation zones, displacing the aqueous phase to create additional CO2 storage capacity. The foam system utilises the Jamin effect to enhance sweep efficiency, effectively reducing residual oil saturation through improved fluid diversion. Foam-oil interactions trigger foam collapse, releasing surfactants that emulsify and strip oil from rock surfaces, further enhancing oil recovery. Compared to CO2 flooding, the proposed foam technology improves oil recovery by 23.92% and CO2 storage efficiency by 59.69%. Compared to conventional foam flooding, oil recovery efficiency increases by 13.85% and CO2 storage efficiency increases by 19.73%. This study provides a high-efficiency, cost-effective solution for enhancing oil recovery and CO2 storage in high-water-cut reservoirs, with substantial potential for real-world applications.
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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".