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Record W4406916739 · doi:10.1016/j.jrmge.2025.01.017

Investigation of imbibition and strain behavior of Longmaxi shale using fiber Bragg grating sensing

2025· article· en· W4406916739 on OpenAlexaff
Yongsheng Tan, Qi Li, Lifeng Xu, Liang Xu

Bibliographic record

VenueJournal of Rock Mechanics and Geotechnical Engineering · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsImbibitionFiber Bragg gratingOil shaleStrain (injury)Materials scienceFiberComposite materialGeotechnical engineeringGeologyOptoelectronics

Abstract

fetched live from OpenAlex

The hydration and swelling of clay in shale reservoirs are important factors for the design of drilling and fracturing fluids. Previous studies show that hydration and expansion are among the main reasons for water imbibition. However, few studies have been carried out on imbibition and strain behavior for the shale of the Longmaxi Formation in Changning, China. In this study, a method based on fiber Bragg gratings for evaluating imbibition and strain behavior is presented. Using this method, three imbibition experiments at different solution concentrations were carried out on the shale samples. The main influencing factors include response characteristics during imbibition, strain response during imbibition, ion concentration of imbibed brine, and the relation between saturation and volumetric strain. The results show that water imbibition can be distinctly categorized into two stages: the initial stage of imbibition is characterized by a dependency on the square root of time, and the water imbibition is a linear function of time. The final water saturation after 250 h of imbibition varies from 54.7% (20 wt% NaCl) to 87.8% (deionized water). As the concentration of NaCl increases, the disparity among the horizontal strain, vertical strain, and volumetric strain diminishes. The saturation and volumetric strain have a strong logarithmic relationship. This study provides a quantitative characterization method of imbibition expansion behavior based on optical fiber sensing, which can realize simultaneous monitoring and characterization of imbibition and strain and provide the basis for the shale imbibition mechanism and fracturing fluid flowback optimization.

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 score0.604
Threshold uncertainty score0.278

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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