Fluid identification in fractured media with genetic algorithm

Abstract

The fluid identification of fracture media is susceptible to factors such as observation data, fluid sensitivity parameters, and inversion algorithms. However, appropriate nonlinear algorithms can be applied to reduce errors generated during the inversion process, thus helping to improve the reliability of fluid identification. In this paper, analysis and comparison are made to various reflection coefficient approximate formulas, of which the applicability and accuracy are demonstrated in fluid-saturated poroelastic media, and the objective function is constructed further from Russell's reflection coefficient formula. In this sense, the genetic algorithm (GA) can achieve the pre-stack inversion of seismic waves. Based on the inversion results such as P-, S-wave velocity, and density, the Russell fluid indicator, Poisson's ratio, and Poisson impedance are combined to identify the water-, gas-, and oil-bearing fracture. Finally, the applicability and reliability of the method are demonstrated through numerical calculations in some regions of Marmousi2 model and the field data. The results show that the seismic data can be preliminarily analyzed by the inversion method, followed by combining the Russell fluid indicator, Poisson's ratio, and Poisson impedance, effectively reducing the multiplicity of the inversion results and improving the reliability of fluid identification in fractured media.