Estimation of Fracture Stiffness, In Situ Stresses, and Elastic Parameters of Naturally Fractured Geothermal Reservoirs

摘要

Knowledge of fracture stiffness, in situ stresses, and elastic parameters is essential to the development of efficient well patterns and enhanced geothermal systems. In this paper, an artificial neural network (ANN)-genetic algorithm (GA)-based displacement back analysis is presented for estimation of these parameters. Firstly, the ANN model is developed to map the nonlinear relationship between the fracture stiffness, in situ stresses, elastic parameters, and borehole displacements. Atwo-dimensional discrete element model is used to conduct borehole stability analysis and provide training samples for the ANN model. The GA is used to estimate the fracture stiffness (k(n), K-s), horizontal in situ stresses (sigma(H), sigma(h)), and elastic parameters (E, v) based on the objective function that is established by combining theANNmodel with monitoring displacements. Preliminary results of a numerical experiment show that the ANN-GA-based displacement back analysis method can effectively estimate the fracture stiffness, horizontal in situ stresses, and elastic parameters from borehole displacements during drilling in naturally fractured geothermal reservoirs. (C) 2014 American Society of Civil Engineers.