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Record W2097784575 · doi:10.3968/7621

Experimental Study of Fracture Propagation Mechanisms by Oriented Perforation Technology for SRV Fracturing

2015· article· en· W2097784575 on OpenAlexvenueno aff
Xin Chang, Huaidong Wang, Yuanfang Cheng, Xiuting Han

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPerforationAzimuthHydraulic fracturingFracture (geology)GeologyGeotechnical engineeringPetroleum engineeringMaterials scienceComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

Due to the growing importance of shale oil and gas resources, more effective techniques to develop unconventional reservoirs are strongly required. In this paper, a new volumetric fracturing measure for unconventional resources based on oriented perforation technology has been introduced. Moreover, this innovative approach was validated through a series of laboratory experiments varying the parameters of physical models, such as perforation azimuth, perforation length and horizontal stress difference. The results showed that the artificial hydraulic fracture formed by using oriented perforation technology was not a satisfactory straight dual-wing fracture, instead, it was a bent dual-wing crack like “S” or “X”, which could significantly increase the stimulated reservoir volume. The perforation azimuth and horizontal stress difference were the primary factors that affected the formation of multi fractures. The reorientation path was extended with the increasing of the oriented perforation azimuth angle, but decreased with the increasing of horizontal stress difference. In addition, increasing the perforation length could greatly decrease the initiation pressure. Therefore, the oriented perforation technology could be as a powerful way to accelerate the exploitation of the unconventional reservoirs.

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: none
Teacher disagreement score0.655
Threshold uncertainty score0.498

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 communication0.0000.001
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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
Published2015
Admission routes1
Has abstractyes

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