Case Study: A Fast Calculation Method on Optimizing Inflowing Control Parameters for Staged Premium Screen
Bibliographic record
Abstract
Staged premium screen technology has been widely used in Shengli Oilfield, and works as a reliable method for sectional inflow control completion in horizontal wells. The completion plan of staged premium screen were originally optimized by flow loop test, however, the test procedure is usually as long as 12 working days. Therefore, a fast calculation method with processing time of no more than 2 days is proposed. When the well bore segmentation scheme is available, screen number as well as base pipe hole density are selected as the key parameters, and are optimized from one interval to the next on the basis of inflow control characteristics formula of staged premium screen. During the optimization of each interval, the parameters which best fit the requirements of “reasonable inflow control pressure drop” and “maximum discharge area” are determined from many alternative values. G6-P15 well, designed under the guidance of fast calculation method, obtained more balanced fluid and oil production profile versus Reference Well G6-P12. So far, 15 wells have been designed by this fast method. On average, the annual decline rate of daily oil production, the annual increase rate of water cut as well as annual mean of water cut of application wells are observed to be 36.7%, 9.4% and 7.9% lower than those of reference wells, respectively, while the annual accumulation oil production of target wells is 12.2% higher than that of reference wells. These successful applications prove that the fast calculation method has provided reasonable design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".