Modeling of CoSolvent Assisted Chemical Flooding for Enhanced Oil Recovery in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Many attempts have been made to understand, design, and optimize a chemical flooding process; however, the current low oil price environment makes its implementation very challenging from an economics point of view. Recently, CoSolvent Assisted Chemical Flooding (CACF) has been considered as a promising approach to reduce the cost of surfactant-based recovery methods, especially in heavy oil reservoirs. More importantly, recent studies indicated that CACF can be efficiently applied at relatively low temperature, i.e., without the need of steam injection. This helps reduce for the cost of steam generation and injection, and the associated greenhouse gas effects. This paper presents a new development in modeling CACF using an Equation-of-State (EOS) compositional reservoir simulator. We used a new approach to model the behavior of the oil-water-microemulsion system based on solubility data without modeling type III microemulsion explicitly. The results showed an excellent agreement with numerous chemical coreflooding data and are in agreement with a chemical floodingresearch simulator. The new development presented includes the effects of cosolvent on rheological properties and phase behavior of microemulsion in the CACF process, particularly microemulsion viscosity and interfacial tension. The proposed model showed good agreement with four published CACF coreflood experiments in which surfactant was not used in alkali and polymer chemical slugs. This model efficiently captures the complex chemical reactionsoccurring in the CACF process, i.e., generation of in-situ soap based on reactions between alkali and a rich acid component in heavy crude oil. The model provides consistent results with laboratory coreflood data at different operating temperatures, which is very important for heavy oil reservoirs. The ultimate recovery factor by CACF coreflooding is about 97%, similar to ASP (Alkali, Surfactant and Polymer) coreflooding, but without the need of surfactant injection.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".