We have performed a quantitative, joint interpretation of 3D seismic and 3D CSEM data from the Troll Western Oil Province. The presented methodology results in 3D distributions of effective porosity and hydrocarbon saturation. The estimated reservoir property distributions correlate with expected production effects, and we show how hydrocarbon volumes can be assessed.
SPECIAL SECTION: M a r i n e a n d s e a b e d t e c h n o l o g y Field appraisal and accurate resource estimation from 3D quantitative interpretation of seismic and CSEM data T he key questions in field appraisal are: What is the hydrocarbon volume, and how are the hydrocarbons distributed in the field? The ability to answer these questions accurately is critical for deciding whether to produce a field and for developing a production plan. Wells drilled during the appraisal phase provide well and flow-test data, which are combined with structural knowledge from seismic surveys to map the extent of the field and generate a reservoir model. The cost for appraising an offshore field can exceed US $100 million, and it is desirable to obtain the information required with fewer wells if possible. Quantitative interpretation of surface geophysical data provides reservoir properties between well locations and can, therefore, significantly reduce appraisal costs. A quantitative analysis of seismic data using well-log information will typically determine reservoir rock porosity. Other important parameters are hydrocarbon saturation, permeability, and net-to-gross ratio. Quantitative interpretation of several reservoir properties using only the seismic data is
The field is located in the Persian Gulf and has been producing for the last 30 years with a strong natural aquifer support. The clastic reservoir exhibits highly heterogeneous permeability combined with shale streaks and therefore presents complex flow behavior.This paper describes the iterative seismic to simulation workflow followed to create a fine scale reservoir static and dynamic simulation model consistent with all available engineering, geologic and geophysical data. The process involved integration of static and dynamic modelling workflows. History matching the production data indicates locations with incorrect information in the static model, which can be corrected and re-exported for the dynamic model in very short time. The integration of static and dynamic modelling is seen as essential for the further commercial development of the field.
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