Overcome Viscous Fingering Effect in Heavy Oil Reservoirs by an Optimized Smart Water Injection Scheme
Bibliographic record
Abstract
Abstract Viscous fingering is a major obstacle to successful waterflooding in heavy oil reservoirs, as it results in water breakthrough and an undeveloped oil bank. To overcome viscous fingering, the composition of injected fluid can be tailored to a production scheme which optimally enhances oil recovery. Smart water can improve oil recovery through wettability alteration. However, wettability alteration also leads change in mobility ratio, which depending on value, may have either a negative or positive impact on oil recovery and water production and must therefore be carefully controlled. In this study, smart waterflooding outperforms conventional waterflooding regarding oil recovery, with incremental recovery reaching as high as five percent. Moreover, smart waterflooding also significantly decelerates the water cut (WCUT) trend by subduing the effect of viscous fingering and decreasing the water relative permeability. Our results show that reducing a salinity of the injected fluid allows mineral dissolution reactions to raise effective permeability compared to that achieved by conventional waterflooding. Injection and production pressure affect mineral dissolution/precipitation and, consequently, effective porosity and permeability. Numerical simulation is used to analyze the potential of smart waterflooding as an enhanced oil recovery method in heavy oil reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".