Big-data-driven pre-stack seismic intelligent inversion

Abstract

Seismic inversion is an image formation process for the spatial structures and physical properties of underground rock strata according to the seismic data observed on the surface or in wells under the constraints of known geological laws and drilling and logging data. It is an important component of geophysical inversion. The inversion of reservoir parameters using seismic data is a non-linear problem. Therefore, the use of linear or quasi-linear methods to solve this problem makes it easy to fall into local optimal solutions. In contrast, intelligent optimisation algorithms based on the global optimum have strong local and global optimisation abilities and good convergence. Thus, they can improve the calculation efficiency and are suitable for geophysical inversion problems with multiple parameters and multiple extremums. In this study, the pre-stack seismic inversion problem is considered as the research object. According to the non-linear characteristics of the problem, the pre-stack seismic intelligent inversion method with a hybrid genetic algorithm is proposed, which solves the problems for which the standard genetic algorithm may easily fall into local optimums, resulting in an indistinct inversion effect, especially an unsatisfactory optimisation effect for density parameters. The experimental results show that the inversion parameters fit well with the theoretical model logging curve. In view of the high data volume in inversion, a new parallel strategy is proposed and combined with the MapReduce framework. This strategy can reduce the operating time while maintaining the diversity of the population. The strategy is implemented on the Hadoop platform and its effectiveness is verified.