Investigation and Application of Perforation Optimization Method on Shale Gas Horizontal Well with Numerical Simulation of Multicluster Fracturing under Dense-Segment Pattern
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Multicluster fracturing of horizontal wells has evolved into a mature and widely adopted technique for exploiting unconventional oil and gas fields. A well-designed multicluster completion strategy can yield an ideal fracturing outcome, significantly enhancing production rates and potentially delivering substantial economic benefits. Nevertheless, empirical evidence suggests that fractured horizontal wells frequently exhibit pronounced nonuniform production profiles, a prevalent issue stemming from the irregular geometry of propagated fractures. This issue critically constrains production rates. To mitigate the adverse effects of low-uniformity fracture propagation, it is imperative to elucidate the factors influencing uniformity levels and their corresponding patterns. Despite extensive discussions on hydraulic fracture propagation mechanisms and optional factors in hydraulic fracturing engineering, there exists a notable oversight regarding the optimization of perforation parameters to achieve improved fracturing uniformity during well completion procedures. This paper introduces an optimization method for perforation parameters based on a fully coupled pseudo-3D numerical model of multicluster fracturing. The impact patterns of cluster spacing, perforation number, and initial perforation diameter on multifracture propagation results and uniformity levels are thoroughly examined. The multicluster fracturing model, developed using the displacement discontinuous method (DDM), is coupled with material balance, pressure transmission, hole erosion computation, and initiation asynchrony estimation. To quantify the uniformity level of the fracturing result, the modified propagation uniformity index ( U fm ) is employed. Simulation results from 20 cases are categorized into six groups based on varied changing patterns of perforation parameters, leading to the identification of five recommendations for optimizing perforation parameters. By implementation of the discussed optimized perforation parameters, successful fracturing outcomes were realized.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".