Estimation of Mechanical Properties of Mancos Shale Using Machine Learning Methods

Abstract

ABSTRACT: We propose the use of balanced iterative reducing and clustering using hierarchies (BIRCH) combined with linear regression to predict the reduced Young’s modulus and hardness of highly heterogeneous materials from a set of nanoindentation experiments. We first use BIRCH to cluster the dataset according to its mineral compositions, which are derived from the spectral matching of energy-dispersive spectroscopy data through the modular automated processing system (MAPS) platform. We observe that grouping our dataset into five clusters yields the best accuracy as well as a reasonable representation of mineralogy in each cluster. Subsequently, we test four types of regression models, namely linear regression, support vector regression, Gaussian process regression, and extreme gradient boosting regression. The linear regression and Gaussian process regression provide the most accurate prediction, and the proposed framework yields R2 = 0.93 for the test set. Although the study is needed more comprehensively, our results shows that machine learning methods such as linear regression or Gaussian process regression can be used to accurately estimate mechanical properties with a proper number of grouping based on compositional data. 1 INTRODUCTION The hydro, mechanical, and chemical properties of shale formations with compositional and textural heterogeneity across a range of scales give rise to very complex behavior under various environmental and engineered conditions. Various geologic variables, including mineralogy, types of cement, organic content, and the spatial distribution of these characteristics, contribute to mechanical properties (elastic properties, fracture toughness, anisotropy, etc.). These compositional and structural heterogeneity in very-fine sedimentary rocks may affect the onset and propagation of brittle fracture in shale and can lead to the formations of flow conduit. Given the formation and operational conditions (e.g., stress, natural fractures, injection fluid and pressure) the geometry and extent of fracture networks is predominantly determined by shale mechanical properties.