Evaluating Efficacy of the Separate Layer Water Injection of the Wells by Using Fuzzy Comprehensive Evaluation Method
Bibliographic record
Abstract
It can directly reflect the effect of injection wells before and after the layering water injection by evaluating the layering water injection wells. In view of the uncertainties on the impact of various factors of injection well group, it can establish a series of quantitative method which is suitable for the evaluation of well group. Prior to the evaluation of water injection wells group, it is necessary to make evaluation of the existing wells. This study uses the fuzzy comprehensive evaluation method to calculate comprehensive evaluation coefficient of well group to judge the nature of each well group. According to the calculation of the comprehensive evaluation coefficient of E2 which is combined with the actual geological and development condition of Ba fault block 17, it can not only find the increasing relation between E2 values and water injection effect, but also evaluate the effect of water injection well group, which can determine whether symmetry layering water injection is reasonable or not. Key words: Layering water injection; Quantitative methods; Evaluation; Nature factor; Fuzzy comprehensive evaluation Key words: Layering water injection; Quantitative methods; Evaluation; Nature factor; Fuzzy comprehensive evaluation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".