An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

Abstract

Summary The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimization algorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological‐evolution–based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm‐intelligence‐based method‐Particle Swarm Optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki‐Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.

An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

“…The higher the absolute value of the correlation coefficient is, the stronger the correlation; the closer the correlation coefficient is to 1 or −1, the stronger the correlation; and the closer the correlation coefficient is to 0, the weaker the correlation. The correlation coefficient function is Formula (24).…”

“…This is then used to analyse the lithological characteristics and physical parameters above and below the reflection interface, and further predict and judge the fluid properties and lithology of the reservoir [16][17][18][19]. Pre-stack seismic data contains numerous useful information that can be used to predict underground oil and gas conditions, of which three elastic parameters, i.e., P-wave velocity V p , S-wave velocity V s , and density ρ, are key parameters that indirectly reflect the saturation state of underground oil and gas [20][21][22][23][24][25]. By using the AVO information to solve the approximate formula of the Zoeppritz equation, the pre-stack inversion obtains the elastic parameters that reflect the characteristics of underground rock directly, i.e., P-wave velocity, S-wave velocity and density.…”

“…In the mid-1980s, nonlinear global intelligent optimisation inversion techniques began to receive the attention of researchers in the geophysical field. New ideas and methods in other areas have been continuously introduced since that time and have many successful applications [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Identification of Dynamic Models by Using Metaheuristic Algorithms

“…[36]. It is also worthwhile to note that Qinghua Wu, et al, confirmed that improved PSO algorithm could markedly enhance inversion precision as well as rendering high correlation coefficients linked with elastic parameters [37].…”

Special issue on multimodal information learning and analytics on cross‐media big data

“…A tire‐ice traction model is established based on a neural network and the finite‐element method by Zhang et al Wu et al propose a swarm‐intelligence–based method, particle swarm optimization algorithm, to handle the elastic parameter inversion problem. An integrated fault diagnosis reasoning strategy based on fusing rough sets, neural network, and evidence theory is presented by Yang and Yu using the principles of data fusion and meta‐synthesis.…”