Information content in 4D seismic data: Effect of correlated noise

Abstract

Several types of data used in history matching for subsurface reservoir characterization have errors that are spatially or temporally correlated. Although it is often assumed that correlated observation errors decrease information content, using a simplified flow problem, we show that for data that are spatially dense (such as 4D seismic data), correlated observation errors result in higher information content than data with similar levels of errors, but without correlation. Unfortunately, correlations in the observation error are often unrecognized and difficult to estimate, especially if the correlation scale of the errors is similar to a characteristic length of the signal. In addition, many history matching algorithms are incapable of accounting properly for correlated observation error, so data are either thinned or the observation error is inflated to partially account for the lack of proper treatment. We show that neglecting correlations in the observation errors or inflating the variance, result in loss of information content. Finally, we show that it is possible to iteratively estimate the correlated observation errors through analysis of residuals after history matching. • Measurement errors in seismic data are generally correlated. • Correlated errors can increase information content when measurements are dense. • Neglecting correlations during model calibration results in information loss. • Correlations in seismic data can be estimated from data mismatch after calibration.