Influence of Filling Medium on Water Injection in Fracture-Vuggy Reservoir
Bibliographic record
Abstract
The Tahe oilfield is characterized with high degree of filling medium in an underground caves and shortage of bottom water in some unites. In addition, it has poor oil displacement effect with water injection, and low recovery rate. Considering geological interpretation results, the cave filling models with different filling mediums are designed. Through physical simulation experiments, the displacement mechanism between the un-filled areas and the filling medium were fully studied. Meanwhile, the effect of filling medium’s wettability, pore size and fluid elastic energy on the oil-water displacement efficiency were clarified. The results showed that: In the process of oil-water displacement, the capillary resistance in the oil-wet filling medium may hinder the oil-water gravity differentiation, making it impossible for the oil in the filling medium to be replaced effectively, so there is a large amount of remaining oil in the filling medium. The stronger the oil wettability of the filing medium and the smaller pore size, the lower the displacement efficiency. Increasing injecting pressure could only increase the formation energy and improve oil production at the beginning, but it could not improve oil displacement efficiency in filling medium. Injection of surfactant could reduce the oil-water interfacial tension and improve the oil phase utilization degree of the filling medium effectively. Field application has the same results with the experiment, so the surfactant injection is an EOR method to improve oil displacement for fracture-cave reservoir with high degree of filling medium.
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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 | 0.000 | 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 | 0.000 | 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 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".