Abstract
In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas.
Highlights
Acoustic wave impedance inversion was first introduced by R.O
We propose using Adaptive Particle Swarm Optimization (APSO), a more efficient optimization tool compared to the traditional Particle swarm optimization (PSO), to accelerate Markov chain Monte Carlo (MCMC) simulation by randomly generating many particles in a swarm (Hossen et al, 2009) [18], where every particle contains rock property parameters obtained from well-log data analysis and interpretation
Particle swarm optimization (PSO) is one of the swarm intelligence (SI) algorithms that was first introduced by Kennedy and Eberhart in 1995, which simulates the behaviors of birds flocking randomly while searching for food in space [24]
Summary
Acoustic wave impedance inversion was first introduced by R.O. Lindseth in 1979, who allowed synthetic sonic logs in interpretation [1]. The inversion methods have been significantly improved from a deterministic to stochastic approach, from linear to non-linear modeling, and from poststack to prestack [3,4]. Stochastic inversion could provide us non-unique solutions with the best fit or average of a good fit. Stochastic inversion generates a suite of alternative output impedance models whose synthetics are consistent with the 3D seismic volume (Tarantola, 1987; Grana, 2017) [9,10]. K. Sen used fractal-based initial models combining very fast simulated annealing (VFSA) to improve impedance inversion of prestack data, which addressed frequencies missing compared to deterministic inversion [12].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
