Improved normalization of time-lapse seismic data using NRMS repeatabity data to improve automatic production and seismic history matching in the Nelson field

Seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively for automated inversion. Often, observed seismic data is obtained as a relative measure of the reservoir state or its change. Unless these data are calibrated, we need to normalise them. In this study we use NRMS, a repeatability measure to filter the observed time-lapse data so that normalization is more effective. We apply this approach to the Nelson field. We use three seismic surveys over nine years of production. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data. Two regression equations are derived as part of the analysis. To obtain the first, the whole 4D signature map of the reservoir is used for each interval while in the second, 4D seismic data is used from the vicinity of wells with a good match to production data. NRMS data is used to remove more anomalous measurements. Net:gross and permeability are modified to improve the match. The best results are obtained when the normalization is performed using NRMS filtered maps of the 4D signature. The history match to the first six years of data is reduced by 55 per cent. The misfit for the forecast of the following three years is reduced by 29 per cent. If only production data is used in history matching, the history and forecast misfit reductions are 45% and 20% respectively. In this case the seismic misfit increases by 5 % while in the best case it dropped by 6%. Updating the reservoir by history matching of 4D data gives us better understanding of changes to the reservoir and also reduces risk in forecasting leading to better decisions about reservoir maintenance and infill well targeting.