Estimation of Water Velocity and Receiver Position Changes in Time-Lapse Seismic Using Machine Learning

Abstract

Non-repeatability is one of the main obstacles in time-lapse seismic, as it significantly degrades the interpretation of reservoir-related signals. Correcting the data variations caused by non-repeatability (4D noise) is of paramount importance, which usually requires the estimation of the changing parameter. ln this paper, we propose a Machine Learning (ML) workflow for the quantitative estimation of two types of 4D noise: changes in the speed of sound in water and receiver lateral positions. A synthetic database, modeled from a velocity model estimated from the Brazilian pre-salt, and containing many time-lapse seismic surveys was generated for the supervised training of ML models. lnput samples consist of subsets of common-shot seismograms. We studied many combinations of ML regression algorithms and feature extraction techniques, for scenarios where data is contaminated, or not, with Gaussian random noise. The regression algorithms considered were Fully-Connected Neural Network, Extreme Gradient Boosting, and Bayesian Ridge. Four rectangular crops of the common-shot seismogram were tested as input features: full seismogram, first half of time samples, 11 smallest-offset traces, and the time samples focusing on the first arrivals region. The combination with the best trade-off between accuracy and model complexity is the Bayesian Ridge fed with the 11 smallest-offset traces, which estimated position and velocity time-lapse changes with median accuracy of 0.115 m and 0.017 m/s for the case with Gaussian noise. Besides the correction of repeatability-related variations, our results are useful in the 4D Full-Waveform lnversion which needs accurate parameters to produce good seismic images.