Automated Seismic Acquisition Geometry Design for Optimized Illumination at the Target: A Linearized Approach

Abstract

In seismic exploration methods, imperfect spatial sampling at the surface causes a lack of illumination at the target in subsurface. The hampered image quality at the target area of interest causes uncertainties in reservoir monitoring and production, which can have a substantial economic impact. Especially in the case of a complex overburden, the impact of surface sampling on target illumination can be significant. Targetoriented acquisition analysis based on wavefield propagation and a known velocity model has been used to provide guidance for optimizing the acquisition parameters. Seismic acquisition design is usually a manual optimization process, with consideration of many aspects. In this study, we develop a methodology that automatically optimizes an irregular receiver geometry when the source geometry is fixed, or vice versa. The methodology includes objective functions defined by two criteria: optimizing the image resolution; and optimizing the angle-dependent illumination information. We use a two-step parameterization in order to make the problem more linear and, thereby, solve the acquisition design problem by using a gradient descent algorithm. With simple and complex velocity models, we demonstrate that the proposed method is effective, while the involved computational cost is acceptable. Interestingly, the optimization results in our examples show that the conventional uniform geometry already satisfies the resolution requirement, while optimizing for angle coverage can provide a large uplift and is strongly dependent on the velocity model.

On the Effect of 3D Wave Propagation on 2D Regional‐Scale Velocity Model Building

“…To mitigate this issue, more advanced shooting approaches based on the source blending/deblending (Berkhout et al., 2008) could be considered. The efficient shooting approach shall be further coupled with optimized source/receiver spacing providing good illumination of the geological target (e.g., Abdellaziz et al., 2023; Guo et al., 2023; Krampe et al., 2020; Maurer et al., 2017; Vermeer, 2003; Wu et al., 2022) and supported at the processing stage by regularization approaches that are robust against sparse seismic acquisition (e.g., Aghamiry et al., 2021; D. Li & Harris, 2018; Zand et al, 2024; Zhu et al., 2017). Successful tuning of all these parameters can only be possible if the seismic experiment is prepared for the purpose of reconstruction of certain target using specific imaging technique.…”