Solvent Retrieval During Miscible Flooding in Heterogeneous Reservoirs Using New Generation Nano EOR Materials: Visual Analysis Through Micro Model Experiments
Bibliographic record
Abstract
Abstract Solvent injection has been given attention to enhance oil recovery by sole use or in combination with a thermal method to develop light and heavy-oil fields. To make this process efficient, one needs to retrieve the expensive solvent. This can be achieved by alternative injection of water if the reservoir is homogeneous. In case of heterogeneous reservoirs (fractured carbonates or sands with wormholes), one needs to develop techniques other than viscous displacement to retrieve the solvent diffused into less permeable matrix portion. A method of injecting low temperature steam/water to heat the solvent to vaporize and retrieve it was introduced recently. An alternative is to inject chemical solution to change the wettability and displace the matrix oil/solvent by capillary imbibition. Although it yields lower recoveries, injection of chemical solutions for the same purpose without pre-solvent treatment might be an efficient (more economical) EOR method. A series of 2-D etched glass micromodel (sandstone replica with a fracture) experiments were designed to investigate the mechanics of chemical injection with and without pre-solvent injection. Conventional surfactants (sulfonate series) as well as new generation chemicals (nanofluids, ionic liquids) were tested for this purpose. After testing and screening effective chemicals without pre-solvent injection, the same chemicals were used to retrieve the solvent and recovery additional oil for pre-solvent injected systems. The micromodel was saturated by dyed processed oils and a selected solvent was injected through the fracture. After the solvent was diffused into matrix completely to recover the oil in it, the model was heated mimicking a thermal method to reach the boiling point of the solvent and retrieve it. Following the heating phase, aqueous phase was injected to retrieve the remaining solvent in the liquid or vapor phase. Visual observations on chemical flooding process clarified the complex interactions among different phases considering small-scale heterogeneities. This information can help screen promising chemical and determine the efficiency of chemical injection with and without pre-solvent injection for EOR in heterogeneous sands and carbonates.
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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".