Luiz Antonio Rozendo

Tying seismic data to well logs is a critical step in seismic amplitude processing and interpretation. This process is often repeated throughout the exploration phase to enhance the understanding of well characteristics, such as the time–depth relationship. However, the presence of data noise and inherent uncertainties makes the well-tie task time-consuming and complex. It requires the estimation and careful control of several parameters to ensure reliable results. A workflow was proposed based on segmented global optimization that partially automates the well-seismic tying process by estimating the matching position between the synthetic and seismic traces and automatically aligning them. The matching position was estimated using time-weighted dynamic time warping (TWDTW), which accounts for the maximum allowable perturbation in the velocity log. In conjunction with TWDTW, a global Bayesian optimizer estimated the tie-related parameters. Following this step, the alignment was performed through an optimization-based method that incorporated segmentation to estimate the velocity log perturbation and wavelet phase, constrained by a predefined tolerance. The well logs and seismic traces were segmented, and each segment was aligned using constrained dynamic time warping. This segmentation increased the number of knots available for interpolating the velocity perturbation curve, resulting in a more detailed and refined alignment. The proposed partial automation improved the correlation in all six wells evaluated, with four wells achieving correlation values above 70%, without introducing physically unrealistic distortions.