Experimental investigation of simultaneous water and CO<sub>2</sub> (SWACO<sub>2</sub>) injection for oil recovery in immiscible and near‐miscible conditions: A comparative study
Bibliographic record
Abstract
Abstract A simultaneous water and CO2 injection to a dead crude oil has been performed on sandstone cores to evaluate oil recovery under secondary immiscible, secondary near‐miscible, tertiary immiscible, and tertiary near‐miscible injection modes. It is demonstrated that secondary SWACO2 injection as well as tertiary flood is an effective method for oil/residual oil recovery from oil‐saturated/water flooded porous media. In near‐miscible condition, oil recovery is higher than in immiscible condition because there is another active pore‐scale production mechanism in near‐miscible injection besides volumetric displacement mechanism. In secondary near‐miscible SWACO2 injection, the ultimate oil recovery increases by increasing SWAG ratio from 0.2 to 0.4 but due to some limits, e.g., topological effects, prohibiting contact of injected gas with residual oil in pores, altering SWAG ratio from 0.4 to 0.6 showed no essential effect on ultimate oil recovery. Secondary SWACO2 injection can recover higher fraction of oil than tertiary SWACO2 injection. This higher oil recovery results from the oil being more accessible by injected gas due to less water‐shielding effects. The results of this work can be helpful to better recognition and selection of gas‐based oil recovery methods to be implemented in depleted reservoirs.
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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".