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Particle Swarm Impedance Inversion Method Research And Appicatin

Posted on:2012-05-14Degree:MasterType:Thesis
Country:ChinaCandidate:L WangFull Text:PDF
GTID:2210330338967838Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
Hydrocarbon resources are still important energies which are difficult to replace nowadays or several decades in future. With the development of exploration technology, the exploitation of structural hydrocarbon reservoir whose type is simple has been almost exhausted. So, how to find lithologic hydrocarbon reservoir will be the main research direction. Seismic exploration is an important method to find and determine lithologic hydrocarbon reservoir. Seismic impedance inversion is very useful for reservoir description and interpretation, and it can accurately describe the rock formations and geological structure, provides basis for drilling wells. In many practical engineering problems, their essence can be transformed into optimization. Seismic impedance inversion is a multi-parameter nonlinear optimization problem, its main purpose is to obtain the reflection coefficient of underground medium, and then get the density, velocity and other parameters. In recent years, various non-linear methods are developing rapidly, the traditional nonlinear inversion methods such as Newton method, gradient method and Monte Carlo method, and heuristics inversion methods included genetic algorithm, simulated annealing algorithm, ant colony algorithm and artificial neural network, these new emerging methods bring hope and dawn for seismic inversion.Particle swarm optimization (PSO) algorithm as a new efficient algorithm attracted wide attention by home and abroad scholars. PSO originated from the movement behavior of birds and proposed by American electrical engineer Eberhart and social psychologist Kennedy. PSO is a technique based on swarm intelligence evolutionary computation, its principle is simple and easy to implement, so PSO has become a hot research topic in many fields. Currently, PSO has been applied to neural network training, function optimization, fuzzy system control, and many other fields and have achieved good results. This paper first introduces the topic basis and the general development of seismic inversion and wave impedance inversion, briefly elaborates PSO theory and research status, and analysis the principle of PSO and its implementation flow. In the particle swarm optimization algorithm, the parameters will affect to the results of the algorithm. Therefore, how to select appropriate parameters to obtain a satisfactory solution is a problem to overcome. This article studies the parameters selection of PSO through functions which include different dimensions, discusses the effects of each parameter and the criteria for selection, and provides theoretical guidance and reference for parameters selection.This paper is based on a large number of reading other articles and understanding the theory of PSO algorithm fully, then applies PSO algorithm to seismic impedance inversion. In order to solve the disadvantages of slow convergence and inverse accuracy, proposes a new PSO algorithm with layered constraints. The basic idea of this method is considering the underground structure as layered impedance model, and then adjusts the number of layers and samples of each layer to achieve layered constraint. The calculation shows that the PSO algorithm with layered constraints can overcome the random fluctuations which caused by conventional PSO algorithm, accelerate the convergence speed and improve the inverse efficiency. Finally, this paper utilizes PSO algorithm with layered constraints to velocity and impedance inversion in two-dimensional theoretical model and actual seismic data, the results show that the new algorithm is practical and effective.
Keywords/Search Tags:impedance inversion, particle swarm optimization, layered constraints
PDF Full Text Request
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