Asynchronous Injection-Production Process: A Method to Improve Water Flooding Recovery in Complex Fault Block Reservoirs
Bibliographic record
Abstract
The complex fault block reservoir has the characteristics of small area and many layers in vertical. Due to the influence of formation heterogeneity and well pattern, the situation that “water fingering is serious with water injection, on the contrary, driving energy is low” frequently occurs in water flooding, which makes it difficult to enhance oil recovery. Asynchronous injection-production (AIP) process divides the conventional continuous injection-production process into two independent processes: injection stage and production stage. In order to study oil recovery in the fault block reservoir by AIP technology, a triangle closed block reservoir is divided into 7 subareas. The result of numerical simulation indicates that all subareas have the characteristic of fluid diverting and remaining oil in the central area is also affected by injected water at injection stage of AIP technology. Remaining oil in the central area is driven to the included angle and border area by injected water and then produced at the production stage. Finally, the oil recovery in the central area rises by 5.2% and in the noncentral area is also increased in different levels. The AIP process can realize the alternative change of reservoir pressure, change the distribution of flow field, and enlarge the swept area by injected water. To sum it up, the AIP process is an effective method to improve the oil recovery in complex fault-block reservoir by water flooding.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".