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Record W4412454594 · doi:10.3390/pr13072252

Fracture Competitive Propagation and Fluid Dynamic Diversion During Horizontal Well Staged Hydraulic Fracturing

2025· article· en· W4412454594 on OpenAlexaff
Yujie Yan, Yanling Wang, Hui Li, Qianren Wang, Bo Wang

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-Canada
FundersNational Natural Science Foundation of China
KeywordsHydraulic fracturingGeologyFracture (geology)Geotechnical engineeringPetroleum engineering

Abstract

fetched live from OpenAlex

This study addresses the challenge of non-uniform fracture propagation in multi-cluster staged fracturing of horizontal wells by proposing a three-dimensional dynamic simulation method for temporary plugging fracturing, grounded in a fully coupled fluid–solid damage theory framework. A Tubing-CZM (cohesive zone model) coupling model was developed to enable real-time interaction computation of flow distribution and fracture propagation. Focusing on the Xinjiang X Block reservoir, this research systematically investigates the influence mechanisms of reservoir properties, engineering parameters (fracture spacing, number of perforation clusters, perforation friction), and temporary plugging parameters on fracture propagation morphology and fluid allocation. Our key findings include the following. (1) Increasing fracture spacing from 10 m to 20 m enhances intermediate fracture length by 38.2% and improves fracture width uniformity by 21.5%; (2) temporary plugging reduces the fluid intake heterogeneity coefficient by 76% and increases stimulated reservoir volume (SRV) by 32%; (3) high perforation friction (7.5 MPa) significantly optimizes fracture uniformity compared to low-friction (2.5 MPa) scenarios, balancing flow allocation ratios between edge and central fractures. The proposed dynamic flow diversion control criteria and quantified temporary plugging design standards provide critical theoretical foundations and operational guidelines for optimizing unconventional reservoir fracturing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.002
GPT teacher head0.195
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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