Intelligent Prediction of Uniaxial Compressive Strength Based on Multi-Source Information Fusion

Uniaxial compressive strength (UCS) is a fundamental indicator of formation hardness, playing a vital role in evaluating geomechanical properties during drilling process. Accurate UCS prediction enables real-time assessment of formation conditions, contributing to improved drilling safety and effici...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of advanced computational intelligence and intelligent informatics Ročník 29; číslo 6; s. 1500 - 1506
Hlavní autoři: Li, Quanxin, Dong, Hongbo, Zhang, Youzhen, Fang, Jun, Li, Wangnian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Témata:
Accuracy
Coal mining
Compressive strength
Correlation analysis
Data integration
Drilling
Efficiency
Geomechanics
Informatics
Lithology
Machine learning
Methods
Neural networks
Prediction models
Real time
Variables
Vibration
ISSN:1343-0130, 1883-8014
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Uniaxial compressive strength (UCS) is a fundamental indicator of formation hardness, playing a vital role in evaluating geomechanical properties during drilling process. Accurate UCS prediction enables real-time assessment of formation conditions, contributing to improved drilling safety and efficiency. This study proposes a multi-source data fusion approach that integrates vibration data with conventional drilling parameters to enhance UCS prediction accuracy. To address the inconsistency in time scales between the two data sources, a piecewise cubic Hermite interpolation method is applied for temporal alignment. The fused dataset is then used to retrain an extreme learning machine model. Experimental validation is conducted using data collected from a surface drilling test site. Results demonstrate that the proposed method significantly outperforms single-source prediction models, highlighting the effectiveness of vibration-assisted data fusion in real-time UCS estimation.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p1500