Effect of Miscible and Immiscible CO2 Flooding on Gravity Drainage: Experimental and Simulation Results
Bibliographic record
Abstract
Abstract Implantation of EOR processes in naturally fractured reservoirs is a challenging task due to preence of extereme heterogeneity. Among potential EOR techniques for implementing in these environments application of CO2 for displacement purposes and/or pressure maintenance might be a viable option. This paper presents the results experimental and simulation studies of the effect of injecting CO2 into fractured media and its influence on oil recovery. Both miscible (i.e. high pressure) and immiscible (i.e. low pressure) schemes for CO2 are investigated in a core of 100 md permeability. The results obtained from these experiments are used to compare the effect of miscible versus immiscible conditions. For these experiments a special stainless steel holder was designed that allows an open space around a core of 30 cm long and 5 cm in diameter, simulating a matrix with surrounding fractures. Carbon dioxide and normal decane were used as solvent and oil, respectively. The core weas saturated with oil with and without presence of residual water saturation. While all experiments were conducted at constant temperature of 35 °C, six series of experiments were carried out at 250, 500, 750, 1000, 1250, and 1500 psi. A high pressure calibrated glass-gauge was connected to the bottom of the fractured media model allowing the produced oil be collected and measured continuously under the pressure and temperature conditions of the experiments. Analyzing the results show that injecting CO2 at higher pressures improves the recovery factor of gravity drainage mechanism in fractured media, significantly.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".