Research of Surfactant-polymer Flooding Response Characteristic and Mobility Optimization of J Oilfield in Bohai Bay
Bibliographic record
Abstract
Surfactant-polymer flooding technology which used in J oilfield is still the first time in Bohai bay, the reference materials are very seldom for its response characteristic and project optimization. Since there’s no blank water flooding stage between polymer flooding and surfactant-polymer flooding in J oilfield, it’s difficult to accurately judge the response characteristic of production wells and injection wells by the conventional method; on the other side, as surfactant-polymer flooding gradually entered the end stage, the effect of decrease water and increase oil became worse, there’s urgently need to improve the effect of chemical flooding. Thus, the research of response characteristic and mobility optimization are conducted in this article. The water cut funnel method is used for the first time to recognize the response of the production wells in J oilfield, and to use the Hall curve method to recognize the response of the injection wells. Meanwhile, based on the idea of mobility control, the minimum polymer concentration which is needed to control the mobility of surfactant-polymer flooding is studied, and establish the mobility control template, the effect of the surfactant-polymer flooding is improved effectively by use of the template to guide the optimization of the polymer concentration, and daily production increase about 15% of J oilfield. The research can be used to guide and refer to other similar offshore oilfield development.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".