Machine Learning and Data Fusion Approach for Elastic Rock Properties Estimation and Fracturability Evaluation

Abstract

Accurate rock elastic property determination is vital for effective hydraulic fracturing, particularly Young's modulus due to its link to rock brittleness. This study integrates interdisciplinary data for better predictions of elastic modulus, combining data mining, experiments, and calibrated synthetics. We used the microstructural insights extracted from rock images for geomechanical facies analysis. Additionally, the petrophysical data and well logs were correlated with shear wave velocity (Vs) and Young's modulus. We developed a machine-learning workflow to predict Young's modulus and assess rock fracturability, considering mineral composition, geomechanics, and microstructure. Our findings indicate that artificial neural networks effectively predict Young's modulus, while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies. Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment. Notably, fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals. In conclusion, this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability, aiding hydraulic fracturing design optimization through diverse data and advanced methods.