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Record W4312426254 · doi:10.23880/ppej-16000306

Hydraulic Fracturing Process Systems and Fluids: An Overview

2022· article· en· W4312426254 on OpenAlexaboutno aff
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Bibliographic record

VenuePetroleum & Petrochemical Engineering Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic fracturingPetroleum engineeringNatural gasPetroleumFossil fuelRevenueUnconventional oilProcess (computing)Oil and natural gasProduction (economics)EngineeringEnvironmental scienceBusinessNatural resource economicsGeologyWaste managementEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Hydraulic fracturing has become a critical component of global petroleum and natural gas development, with most the countries around the globe, for example, Canada, India, England, and China actively pursuing the implementation of this technology to increase oil production after declination as well as tap into this new source of energy. Hydraulic fracturing has created jobs and increased revenue in several states across the country. However, as with any advanced technology, there are concerns about its long-term environmental impact. Thus, many researchers and technicians continuously conduct advanced studies to inform industries about any new or upcoming regulations. In this study, a mini-review of the fracking process is considered an important section of the petroleum and natural gas industries. Moreover, researchers demonstrated knowledge about the frac systems and different hydraulic fracturing fluids that are utilized for a fracking job which were different from one fracking system to another in addition to the nature of the reservoir formation. It is a significant factor that production engineers take into consideration when applying hydraulic fracturing to enhance oil or gas production and treat the formation damage, as well. Since the formation damage considers the most critical issue affecting oil and gas production due to fine migration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

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 integrity0.0000.002
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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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