Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow ...Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.展开更多
The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kin...The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kinetic parameters used for deciding pyrolysis rate. A repulsive particle swarm optimization algorithm is used to obtain the pyrolysis-related properties. In the previous study all properties obtained only using a cone calorimeter but in this paper both the cone calorimeter and thermo gravimetric analysis (TGA) are used for precisely optimizing the pyrolysis properties. In the TGA test a very small mass is heated up and conduction and heat capacity in the specimen is negligible so kinetic parameters can first be optimized. Other pyrolysis-related properties such as virgin/char specific heat and conductivity and char density are also optimized in the cone calorimeter test with the already decided parameters in the TGA test.展开更多
文摘Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.
文摘The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kinetic parameters used for deciding pyrolysis rate. A repulsive particle swarm optimization algorithm is used to obtain the pyrolysis-related properties. In the previous study all properties obtained only using a cone calorimeter but in this paper both the cone calorimeter and thermo gravimetric analysis (TGA) are used for precisely optimizing the pyrolysis properties. In the TGA test a very small mass is heated up and conduction and heat capacity in the specimen is negligible so kinetic parameters can first be optimized. Other pyrolysis-related properties such as virgin/char specific heat and conductivity and char density are also optimized in the cone calorimeter test with the already decided parameters in the TGA test.
