Seismic and sparse data integration through the use of direct sampling
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The integration of seismic attributes and well data is an important step in the development of reservoir models. These models draw upon large data sets including information from well logs, production history, seismic interpretation, and depositional models. Modern integration techniques use the extensive data sets to develop precise models using complex workflows at increased cost of time and computational power. However, a gap exists in which a geostatistically driven procedure could integrate pattern statistics inferred from seismic images and those integrated from analogous geologic systems in order to develop spatially accurate reservoir models. Direct Sampling Seismic Integration Process, DSSIP, was first proposed by Henke and Srinivasan (2010) as an alternative to traditional seismic integration methods. The process provides a probabilistic mapping tool for fast reservoir analysis based on sparse conditioning data in a target reservoir and fully interpreted data from an analog field. DSSIP combines the structural information present in seismic data and facies patterns present in a training reservoir to create a fully realized output map for the target field. In this work, the basic DSSIP algorithm has been further optimized by performing a detailed parameter sensitivity study. The basic DSSIP algorithm has been demonstrated for a real field data set for a deepwater Gulf of Mexico reservoir. The basic DSSIP algorithm has also been analyzed to understand and model the effects of features such as salt canopy that can blur the seismic image. Finally, a modification to the basic algorithm is also presented that uses only a training model and the seismic data for the target reservoir in order to generate reservoir models for the target reservoir. This procedure eliminates the requirement to have a matching pair of training data sets for both the facies distribution and the corresponding seismic response.