APPLICATION OF COMPUTATIONAL INTELLIGENCE METHOD IN PETROLEUM GEOMECHANICS CHARACTERIZATION

Abstract

Petroleum geomechanics characterization refers to the process of quantitatively assigning geomechanical parameters using all available field data. In this paper, an attempt is made to develop a computational intelligence method that integrates genetic algorithm (GA) and artificial neural network (ANN). Through this method, these geomechanical parameters such as horizontal in situ stresses, fracture aperture and joint spacing are determined based on the borehole displacements during drilling well. In the hybrid ANN–GA model, GA can automatically identify geomechanical parameters as neural inputs from monitored borehole displacements, and the ANN is trained to learn the complex relationship between geomechanical parameters and target borehole displacements. Data from numerical experiments on petroleum wells are used in verification of the proposed computational intelligence method for geomechanics characterization. The study of numerical experiment illustrates that the proposed computational intelligence method has the ability to generate reliable results.