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Record W4395451044 · doi:10.1063/5.0198010

Study on rock strength weakening in multi-stage acid fracturing using continuous strength test

2024· article· en· W4395451044 on OpenAlexaff
Qing Wang, Fujian Zhou, Hang Su, Siyu Zhang, Fuwei Yu, Rencheng Dong, Junjian Li, Zhangxin Chen

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsAcid strengthHydrochloric acidPermeability (electromagnetism)Fracture (geology)Materials scienceComposite materialChemistryMetallurgyCatalysis

Abstract

fetched live from OpenAlex

Multi-stage acid fracturing can boost productivity in low-permeability limestone reservoirs, with success hinging on differential etching and the strength of undissolved regions to keep fractures open. Traditional rock strength test methods have strong randomness and error. This study explores the influence of four acid systems (hydrochloric acid, single-phase retarded acid, gelled acid, and emulsified acid) on fracture surface strength based on a new continuous strength test method. The rock strength weakening variation under different acid types and injection conditions was quantified, and the mechanism of single-phase retarded acid slowing down rock strength reduction was revealed. The results indicated that the fracture surfaces were reduced to a lesser extent than in traditional rock mechanical failure studies. Hydrochloric acid caused up to 28% of rock strength depletion, followed by 23% for gelled acid, 18% for emulsified acid, and 11.8% for single-phase retarded acid. Adjusting the acid injection parameters revealed that longitudinal leak-off at the fracture surface changes the rock's strength failure tendency. The microscopic results confirmed that the appropriate acid-rock reaction rate and viscosity are beneficial in reducing strength by forming the dominant wormhole that “siphons” the subsequent acid more profoundly into the formation, thereby reducing the reaction of the acid with the fracture surface. This study can help to understand better the mechanism by which acid reduces the strength of fracture surfaces and can provide guidance for selecting appropriate acid fluids for acid fracturing in low-permeability limestone 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 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 score0.079
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 integrity0.0000.001
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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

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