On the relationship of Poisson’s ratio with geophysical characteristics of rocks

Abstract

Currently, there are no reliable universal dependencies of the Poisson’s ratio on the geomechanical and geophysical characteristics of productive objects. This study aims to investigate the influence of geophysical parameters on the Poisson’s ratio for oilfield productive objects using machine learning methods. The analysis is based on data from several oil and gas fields, presenting results of dependencies between the Poisson’s ratio and parameters such as natural radioactivity of rocks and P-wave velocity. The elastic parameters were identified through triaxial tests of rock samples under reservoir conditions conducted on the PIK-UIDK/PL triaxial system at Perm National Research Polytechnic University. A brief overview of existing standards for conducting triaxial tests is also provided. The importance of standardizing methods for determining the Poisson’s ratio and other elastic parameters of rocks is emphasized to ensure comparability of research results from different fields and their applicability in various geological conditions. The proposed data analysis method relies on linear regression and machine learning methods to establish stable relationships between the Poisson’s ratio and geophysical parameters obtained from various well logging studies. These dependencies allow for more accurate estimation of the Poisson’s ratio for different types of rocks and fields, representing a significant step in developing methods for assessing elastic properties of rocks in oil fields. The obtained results can be used for more precise modeling and forecasting of oil field development processes, contributing to increased efficiency in hydrocarbon extraction and optimization of production processes in the oil industry.

Keywords

• poisson’s ratio
• geomechanics
• triaxial tests
• machine learning