Weak Gel Flooding Research and Effect Assessment of Horizontal Injection-Production Well Groups in Light Level of Oilfields
Bibliographic record
Abstract
Weak gel flooding has been tested and achieved good results in Bohai heavy oilfields. Based on the mechanism analysis and numerical simulation study, weak gel flooding technology is believed to be effect of increasing oil and decreasing water in light oilfields. BZ S oilfield is a low-viscosity oilfield based on horizontal wells development. Due to different production online time and production rate of the well groups of injection and production horizontal wells, it caused advantageous channels formed between injection and production wells in some groups, which reduce storage rate of injection water and affect the development effect. In order to suppress the injected water onrush along the high permeability layer and improve water-oil mobility ratio and sweep efficiency, we have selected two groups for the weak gel flooding test. After flooding test, we evaluate and analyze the recovery and injection characteristics. It shows the test did not achieve the expected result. Therefore, we sum up reasons for the defeat. First, for horizontal injection wells, weak gel plugged well section of relatively pool physical property around water injection wells, increase of wellhead pressure for injection wells make it difficult to meet the requirements of injection allocation. Second, weak gel is hard to work for the pattern of spacing greater than 400m. For these two reasons, weak gel is not displaced and injected to the deep reservoir, and superior channel is still existed, not present precipitation increased the effect of oil wells. Through the evaluation and failure analysis of this displacement test, we proposed technical requirements for weak gel flooding on the horizontal group of injection and production wells. It provided practical experience and references for the other oilfields EOR tertiary oil recovery programs.
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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) | 0.000 | 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".