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Record W2897385019 · doi:10.3997/2214-4609.201801171

Analysis of the Fracturing Sequence Effect on the Multi-Fracture Propagation in the Horizontal Well of the Tight Sandsto

2018· article· en· W2897385019 on OpenAlexaff
Jun Zhong, Heng Zheng

Bibliographic record

VenueProceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHydraulic fracturingGeologyFracture (geology)Petroleum engineeringGeotechnical engineeringPermeability (electromagnetism)Fracturing fluidPerforationMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Summary The complexities and challenges of unconventional reservoirs necessitate the research on the hydraulic fracturing exploration and development. Multi-stage clustering hydraulic fracture of the horizontal well is an important technique for enhanced oil recovery of low permeability reservoir as it extends the drainage radius. The stresses among perforation clusters is essential for multiple clusters staged hydraulic fracture of sandstone horizontal well. Results from mining field demonstrates that fracturing sequences determine the multiple-fracture distribution. In this paper, we analyzed fracture distribution of the four injection points sequential fracturing by the extended finite element method according to fluid-solid coupling and rock fracturing mechanics. The simulation results show that the fracturing sequence determines the induced stress distribution, thus affecting the effective length of the cracks. Comparing the fracture distribution of the four point sequential fracturing in the horizontal well, we found the optimize way of the sequential fracturing to decrease the impacts of the induced stress on hydraulic fracturing as it increases the possibility of effective cracking.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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