Seismic History Matching of Nelson using Time-lapse Seismic Data: An Investigation of 4D Signature Normalization

Abstract

History matching of a reservoir model is always a difficult task. In some fields we can use time-lapse (4D) seismic data to detect production induced changes as a complement to more conventional production data. In seismic history matching we predict these data and compare to observations. Observed time-lapse data often consists of relative measures of change which requires normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching.We apply the approach to the Nelson field in which four surveys are available over nine years of production. We normalize the 4D signature in a number of ways. Firstly we use predictions of 4D signature from vertical wells that match production and derive a normalization function. As an alternative, we use cross-plots of the full field prediction against observation. Normalized observations are used in an automatic history matching process where the model is updated. We analyse the results of the two normalization approaches and compare against the case of just using production data.The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8% and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods respectively. When we use only production data the production history match is improved to a similar degree (45%) but in predictions, the improvement is only 25% and the 4D seismic misfit is ten percent worse.Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching we can better predict reservoir behaviour and identify regions of remaining oil.