Bayesian wavelet estimation from seismic and well data

Abstract

A Bayesian method for wavelet estimation from seismic and well data is developed. The method works both on stacked data and on prestack data in form of angle gathers. The seismic forward model is based on the convolutional model, where the reflectivity is calculated from the well logs. Possible misties between the seismic traveltimes and the time axis of the well logs, errors in the log measurements, and seismic noise are included in the model. The estimated wavelets are given as probability density functions such that uncertainties of the wavelets are an integral part of the solution. The solution is not analytically obtainable and is therefore computed by Markov‐chain Monte Carlo simulation. An example from Sleipner field shows that the estimated wavelet has higher amplitude compared to wavelet estimation where well log errors are neglected, and the uncertainty of the estimated wavelet is lower.