BPNN-GA-based Intelligence Information Approach to Characterize Deep Fractured Strata

This paper presents a displacement back analysis approach with back propagation neural network (BPNN) and genetic algorithm (GA) to characterize the deep fractured strata. In this method, BPNN is employed to perform the nonlinear relationship between the geotechnical properties and and the wellbore deformation behaviors, and to combine the field monitoring information to establish the fitness function for GA. The learning specimens for BPNN are from the numerical simulation and field measurements. GA is employed to perform multi-objective optimization for determination of geotechnical properties on the basis of the established fitness function in a large search space. Results of characterization of geotechnical properties show that the displacement back analysis approach based on the integration of BPNN and GA can effectively recognize the horizontal in–situ stresses and natural fracture properties from wellbore displacements during drilling. This is significant in deep rock mechanics investigation because accurate and low–cost information on geotechnical properties is critical in reducing well costs and optimizing drilling.