Integrated Experimental and Numerical Investigation on CO2-Based Cyclic Solvent Injection Enhanced by Water and Nanoparticle Flooding for Heavy Oil Recovery and CO2 Sequestration
Bibliographic record
Abstract
Cyclic solvent injection (CSI) with CO2 is a promising non-thermal enhanced oil recovery (EOR) method for heavy oil reservoirs that also supports CO2 sequestration. However, its effectiveness is limited by short foamy oil flow durations and low CO2 utilization. This study explores how waterflooding and nanoparticle-assisted flooding can enhance CO2-CSI performance through experimental and numerical approaches. Three sandpack experiments were conducted: (1) a baseline CO2-CSI process, (2) a waterflood-assisted CSI process, and (3) a hybrid sequence integrating CSI, waterflooding, and nanoparticle flooding. The results show that waterflooding prior to CSI increased oil recovery from 30.9% to 38.9% under high-pressure conditions and from 26.9% to 28.8% under low pressure, while also extending production duration. When normalized to the oil saturation at the start of CSI, the Effective Recovery Index (ERI) increased significantly, confirming improved per-unit recovery efficiency, while nanoparticle flooding further contributed an additional 5.9% recovery by stabilizing CO2 foam. The CO2-CSI process achieved a maximum CO2 sequestration rate of up to 5.8% per cycle, which exhibited a positive correlation with oil production. Numerical simulation achieved satisfactory history matching and captured key trends such as changes in relative permeability and gas saturation. Overall, the integrated CSI strategy achieved a total oil recovery factor of approximately 70% and improved CO2 sequestration efficiency. This work demonstrates that combining waterflooding and nanoparticle injection with CO2-CSI can enhance both oil recovery and CO2 sequestration, offering a framework for optimizing low-carbon EOR processes.
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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 | 0.001 |
| 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".