Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: artificial intelligence approach

Abstract

Abstract The uniaxial compressive strength (UCS) and tensile strength (T0) are crucial parameters in field development and excavation projects. Traditional lab-based methods for directly measuring these properties face practical challenges. Therefore, non-destructive techniques like machine learning have gained traction as innovative tools for predicting these parameters. This study leverages machine learning methods, specifically random forest (RF) and decision tree (DT), to forecast UCS and T0 using real well-logging data sourced from a Middle East reservoir. The dataset comprises 2600 data points for model development and over 600 points for validation. Sensitivity analysis identified gamma-ray, compressional time (DTC), and bulk density (ROHB) as key factors influencing the prediction. Model accuracy was assessed using the correlation coefficient ( R ) and the absolute average percentage error (AAPE) against actual parameter profiles. For UCS prediction, both RF and DT achieved R values of 0.97, with AAPE values at 0.65% for RF and 0.78% for DT. In T0 prediction, RF yielded R values of 0.99, outperforming DT's 0.93, while AAPE stood at 0.28% for RF and 1.4% for DT. These outcomes underscore the effectiveness of both models in predicting strength parameters from well-logging data, with RF demonstrating superior performance. These models offer the industry an economical and rapid tool for accurately and reliably estimating strength parameters from well-logging data.