Upscaling method based on sedimentary cycle prior and its application to well‐to‐seismic tie

Abstract

AbstractA well‐to‐seismic tie is a typical upscaling problem. The traditional well‐to‐seismic tie methods often use the velocity log directly to tie well logs to seismic data without considering the upscaling problem led by the velocity dispersion due to thin beds. We propose an upscaling method based on the Backus average method to upscale the well‐log velocity to seismic velocity and apply it to a well‐to‐seismic tie problem. For Backus average method, choosing an appropriate average window is a crucial issue. We introduce a synchrosqueezing optimal basic wavelet transform to calculate an average window. The synchrosqueezing optimal basic wavelet transform is a time–frequency representation method, which can extract the sedimentary cycle information from seismic data. Considering the relationship between the sedimentary cycle and the thin‐bed thickness, we propose a workflow to estimate the instantaneous thin‐bed thickness based on synchrosqueezing optimal basic wavelet transform and determine the window length of the Backus average at each depth point. Then, the Backus average is used to estimate the effective velocity, which is further used to calculate the initial time–depth function and transfer the reflection coefficients from the depth domain to the time domain. Choosing an appropriate seismic wavelet is another fundamental problem for the well‐to‐seismic tie process. This paper introduces an alternating iterative deep neural network‐based method to estimate the seismic wavelet. Thus realistic synthetic seismograms can be computed by convolving the estimated seismic wavelet and the reflection coefficient, which can be stretched or squeezed to match the near‐well seismic trace. We show that the proposed workflow effectively reduces the errors in a well‐to‐seismic tie procedure caused by velocity dispersion of thin beds.