Abstract

As in many other fields in Statoil, Time-lapse (or 4D) seismic data is one of the important tools used for monitoring the Norne field. However, in order to reduce uncertainties, an integrated multidiscipline approach involving geophysics, reservoir engineering, geology and petrophysics has been successfully applied. As a result, a better history matched reservoir model was obtained by improving the consistency between the reservoir simulator results and the observed 4D seismic data. This led to an increased understanding of the reservoir drainage and hence contributed to the process of identifying and prioritizing infill-drilling targets at Norne.

Introduction

Time-lapse seismic data is used in the reservoir management of about 70% of the offshore fields operated by Statoil. Interpreted and inverted 4D seismic data has given valuable information used to locate remaining hydrocarbons in the reservoirs. Thus, we have been able to save wells, optimize the positions of infill wells and to generally increase the understanding of our reservoirs.

However, there is a need to extract more quantitative predictions from the 4D seismic data. This can be achieved by increasing the integration of geophysical data with data from well logs, well production and injection data and general geological knowledge. One particular approach for data integration is by using all available and relevant data in conditioning the dynamic reservoir model. In other words, to use 4D seismic data jointly with well data in the history matching process of the reservoir simulator.

In this paper an integrated approach applied on the Norne field is presented where several disciplines as 4D Q marine seismic acquisition and processing, 4D seismic Bayesian inversion and computer-assisted seismic history matching are combined together. One of the new aspects of this method is the quantification and the propagation through the whole chain of the uncertainties inherent to seismic and well data.

The final objective is to update the reservoir model and so, obtain a better estimate of the remaining producable volumes that is crucial in reducing decision risks related to the well drilling process.

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