A Machine Learning Approach to Estimate Geo-mechanical Parameters from Core Samples: A Comparative Approach

Abstract

Geo-mechanical parameters and Thomsen parameters play very important roles to design a stable wellbore in a challenging environment. The main objective of this paper is to estimate Thomsen's parameters (ε, γ, δ) and geo-mechanical properties from the core samples by Machine Learning and a comparative analysis with the conventional mathematical approach; to place emphasis on the use of Machine Learning and Artificial Intelligence in the Oil & Gas industry and to highlight its future potential to help in the digital transformation of the industry. Two different Machine Learning models, the Ordinary Least Square method and the Random Forest method, were used to predict the aforementioned geo-mechanical properties from the wave velocity and confining pressure data. In this study, it has been observed that the approaches employed in the estimate of geo-mechanical properties are rapid and reliable (about 93.5 percent accuracy) and may be applied in geo-mechanical modeling for wellbore stability analysis for safe and cost-effective well plan and design on a large scale. The analysis in this work indicates that Young’s modulus and Poisson’s ratio are heavily influenced by the anisotropy parameters. Finally, a comparison is made with mathematical approaches. The machine learning and artificial intelligence approaches shown here are excellently matched with mathematical approaches. The geo-mechanical parameters and Thomsen parameters and be computed with reasonable accuracy with the help of our proposed ML algorithms. Our proposed ML model can predict the geo-mechanical parameters and Thomsen parameters from the velocity profile directly without complex mathematical computation. The mathematical model would have required us to first determine the stiffness constants for the prediction of that parameters. Additionally, we may conclude that a machine learning model needs to be trained with more modeling data to predict the right values with a smaller error margin. The number of data points required to train a model has a significant impact on the model's overall accuracy. Therefore, additional modeling data is needed to learn about and comprehend the intricacies, patterns, and interactions between provided input and output variables.