Core sample selection based on MRGC method utilizing geomechanical units

Abstract

Coring is essential for understanding subsurface rock properties and optimizing reservoir characterization. This study explores the application of the Multi-Resolution Graph-Based Clustering (MRGC) method for efficient sample selection in coring programs. Through a detailed analysis of well logs and core data, focusing on mechanical properties like rock strength and elasticity, geomechanical units are identified using the MRGC method. By aligning selection criteria with program objectives and integrating various data sources, the MRGC method optimizes sample selection, offering a comprehensive insight into subsurface geomechanical properties while minimizing core sample requirements. The systematic and targeted sample selection facilitated by the MRGC method ensures that core samples accurately represent the geomechanical characteristics of different field layers. By incorporating petrophysical logs and geomechanical parameters, a model was developed for formations in Gachsaran, Asmari, and Pabdeh-Gorpi-Gadovan. Ultimately, 11 geomechanical units were distinguished from 12 coring wells based on 4 input parameters using the MRGC method. This method improves sample selection accuracy and efficiency, validates geomechanical unit definitions, and offers valuable insights into subsurface geomechanics.