Bibliographic record
Abstract
Abstract: Shale gas is an important unconventional energy resource. However it’s only achieved successful commercial exploitation in the United States and Canada. Shale gas well QY1 was carried out in southeast Chongqing.Taking this well as a case, the fracturing design optimization workflow and its practice are described in this paper. The fracturing feasibility of well QY1 was firstly determined by referencing all the physical parameters of target formation. Then the reservoir stress model was revised and reservoir stress profile was established as well. The reservoir simulation software (Eclipse) was applied to build reservoir geological model for predicting the gas production and determine the optimized permeability enhanced area (PEA). The unconventional fracturing model (UFM) was used to optimize the fluid volume and fracturing scale. And the final fracturing plan was made based on the optimized conclusions. This optimization workflow and the good result provide reliable technique support for the development of the shale gas reservoir in this area. Introduction of measures well Well QY1, a shale gas exploratory well with evolution well segment from depth of 727 meter to 801.2meter, is located in the Southeastern axis of Sangzeping syncline. Its target layer belongs to
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.018 |
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".