Research Of Wave Impedance Inversion Based On Hybrid Particle Swarm Optimization

With the continuous development of oil and gas exploration technology,the relatively simple structural reservoir has basically dried up.Seismic exploration is an important means to discover and identify lithologic oil and gas reservoirs,and seismic impedance inversion is helpful to reservoir description and later interpretation of seismic data.It can reflect the underground rock strata and geological structure more truly,and provide a reliable basis for drilling.At present,the traditional linear inversion method can not meet the actual demand of inversion,and can not get good inversion results.Therefore,this paper uses the nonlinear inversion algorithm,based on the particle swarm optimization algorithm to improve the fusion,and applies it to the wave impedance inversion,and uses the theoretical model to verify its feasibility and reliability,so that the reservoir prediction can be applied to the actual seismic data inversion in the later period.In this paper,the relevant theory of seismic inversion,the basic principle of wave impedance inversion and the method of seismic wavelet extraction are introduced,and the objective function of the inversion is established.The principle and basic process of particle swarm optimization(PSO)are introduced in detail,and some strategies for the learning factor,maximum speed and inertia weight adjustment of particle swarm optimization are studied,and the advantages and disadvantages of the algorithm are analyzed.In order to make up for the defects of the algorithm,a chaotic particle swarm optimization algorithm based on the linear decreasing inertia weight is proposed by introducing the chaos algorithm and combining the genetic algorithm.Based on the theoretical research of the algorithm,the simulation analysis is carried out in this paper.The selection of inertia weight is very important in particle swarm optimization.In this paper,the selection strategy of different inertia weights is analyzed and simulated.The results show that the linear inertia weight is better than other strategies,and the convergence and accuracy of the algorithm can be improved to a certain extent.Using a variety of test functions to simulate the improved algorithm and compare it with the original algorithm shows that the performance of the improved particle swarm algorithm is better than that of the traditional particle swarm algorithm because chaotic search is used and its pseudo-randomness and traversal are used for reference.Sexuality makes the algorithm have better search ability and solution efficiency,avoids the blindness of traditional particle swarm optimization algorithm,and has a more subtle and continuous local search capability.The improved algorithm also takes advantage of the genetic algorithm to maintain the diversity of the population,it has the ability to not easily fall into the local optimum,showing a good global search capability,while significantly reducing the number of invalid iterations,improving the convergence speed.Finally,the hybrid particle swarm optimization algorithm proposed in this paper is applied to the wave impedance inversion,and the feasibility and accuracy of the improved algorithm for wave impedance inversion are verified by the simulation of a simple and complex seismic model.The algorithm is applied to actual seismic data to achieve wave impedance inversion.