Experimental and Modeling Study of CO2 EOR and CO2 Storage in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract This study focuses on unveiling the interaction between injected CO2 and heavy oil through adequate phase behavior analyses. Moreover, the potential of CO2 EOR and CO2 storage were evaluated through injection scheme optimization and sensitivity analysis. Experimentally, the PVT experiments including CO2/heavy oil systems have been carried out to measure oil swelling, solubility, viscosity reduction, and density variation. The MMP of the heavy oil-CO2 mixture has been determined to provide the reference pressure for core displacements. CO2 injection experiments were conducted to examine the performance of CO2 enhanced recovery under different pressure. Different injection schemes were experimentally simulated including water flood, injection water followed by CO2 flooding, and injection water followed by CO2-WAG (water alternating CO2 flooding). Based on these studies, the sensitivity analysis was run on the validated model to examine the effects of different parameters including gas injection rate, CO2 slug size, and CO2-WAG cycle number on the heavy oil recovery and CO2 storage efficiency. As the saturation pressure of the heavy oil-CO2 mixture increases, the solubility of CO2 in heavy oil, the swelling, and the viscosity reduction increase at reservoir temperature (60°C). Although CO2 displacement efficiency and CO2 storage efficiency increase with increasing injection pressure, the increase in these two factors become significantly slower as pressure exceeds the MMP (30 MPa). Injection water followed by CO2-WAG increased oil recovery more than water flood or injection water followed by CO2 flooding. Only considering the influence of single factor conditions, the higher the injection CO2 rate, CO2 slug size, or WAG cycles number, the higher the cumulative oil production. However, based on comprehensive consideration of oil displacement rate, CO2 storage efficiency, CO2 cumulative storage, and cumulative WOR (water-oil ratio), reasonable injection CO2 rate, CO2 slug size, and WAG cycles number were finally optimized and screened out as 30,000 m3/day, 0.5 PV, and 5, respectively. The outcomes of this work provide valuable information for designing a suitable CO2 flooding strategy in heavy oil reservoir engineering applications. It also could bring significant economic and environmental benefits by improving oil recovery and reducing CO2 emissions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".