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

Ahmed Farid Ibrahim, Moaz Hiba, Salaheldin Elkatatny*, Abdulwahab Ali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)317-329
Number of pages13
JournalJournal of Petroleum Exploration and Production Technology
Issue number1
StatePublished - Jan 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.


  • Failure parameters
  • Logging data
  • Machine learning
  • Tensile strength
  • Uniaxial compressive strength

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • General Energy


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