Enhanced oil recovery ability of branched preformed particle gel in heterogeneous reservoirs
Bibliographic record
Abstract
Reservoir heterogeneity is the main cause of high water production and low oil recovery in oilfields. Extreme heterogeneity results in a serious fingering phenomenon of the displacing fluid in high permeability channels. To enhance total oil recovery, the selective plugging of high permeability zones and the resulting improvement of sweep efficiency of the displacing fluids in low permeability areas are important. Recently, a Branched Preformed Particle Gel (B-PPG) was developed to improve reservoir heterogeneity and enhance oil recovery. In this work, conformance control performance and Enhanced Oil Recovery (EOR) ability of B-PPG in heterogeneous reservoirs were systematically investigated, using heterogeneous dual sandpack flooding experiments. The results show that B-PPG can effectively plug the high permeability sandpacks and cause displacing fluid to divert to the low permeability sandpacks. The water injection profile could be significantly improved by B-PPG treatment. B-PPG exhibits good performance in profile control when the high/low permeability ratio of the heterogeneous dual sandpacks is less than 7 and the injected B-PPG slug size is between 0.25 and 1.0 PV. The oil recovery increment enhanced by B-PPG after initial water flooding increases with the increase in temperature, sandpack heterogeneity and injected B-PPG slug size, and it decreases slightly with the increase of simulated formation brine salinity. Choosing an appropriate B-PPG concentration is important for B-PPG treatments in oilfield applications. B-PPG is an efficient flow diversion agent, it can significantly increase sweep efficiency of displacing fluid in low permeability areas, which is beneficial to enhanced oil recovery in heterogeneous 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) | 0.000 | 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 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".