Optimization of Chock Valves for Fracture Clean-up in Tight Gas Condensate Reservoirs
Bibliographic record
Abstract
Abstract Hydraulic fracturing is a vital technique to unlock tight gas condensate reservoirs. The efficiency of clean-up in tight gas condensate reservoirs has a tremendous effect on well delivery. During hydraulic fracturing flowback operations, only part of fracturing fluids flows back to the surface, resulting in discrepancies between the expected fracture length and the effective production fracture length. A reasonable choke size during fracture clean-up can help to maximize the fracture conductivity. In order to get a maximum amount of fracturing fluids flowing back to the surface and a least amount of proppants flowing back, optimization of a chock valve in operations is investigated. Furthermore, effects of a proppant size and well types including vertical and horizontal wells on chock valve adjustments are presented. A chock is adjusted by gradually increasing its diameter as fractures start to close. In addition, the chock size needs to be bigger in a horizontal well than that in a vertical well under the same conditions. If the fracture width close to a proppant size, the chock size will be bigger as the proppant diameter increases; if the fracture width is much larger than the diameter of the proppants, the chock size will be larger with a larger diameter of proppants prior to fracture closure. However, the optimum chock size will be smaller with a larger size of proppants after fracture closure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".