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

Abstract

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.