Geostatistics for Reservoir Characterization

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SPE Member Abstract Geostatistics and, more specifically, stochastic modeling of reservoir heterogeneities are being increasingly considered by reservoir analysts and engineers for their potential in generating more accurate reservoir models together with usable measures of spatial uncertainty. Geostatistics provides a probabilistic framework and a toolbox for data analysis with early integration of information. The uncertainty about the spatial distribution of critical reservoir parameters is modeled and transferred all the way to a risk-conscious reservoir management. The stochastic imaging (modeling) algorithms allow the generation of multiple, equiprobable, unsmoothed reservoir models yet all honoring the data available. Introduction Numerical models of reservoirs often fails to capture the heterogeneities that are critically important for reservoir performance. With production always being the primary function performance. With production always being the primary function of a well, the available data are typically biased toward the more productive regions in a reservoir and are regrettably sparse. The interpolation and gridding algorithms commonly used by industry further exacerbate the problem since they are low-pass filters that tend to smooth out the little spatial variability that the sparse data reveal. While core plugs and well logs are not the only sources of information, other data, such as geophysical information, are often difficult to integrate since they have different levels of reliability and are representative of very different volumes of rock. History matching on historical production information and well test data does not guarantee reliable production information and well test data does not guarantee reliable forecasts of a reservoir's future performance. All of this is not news; these problems have come to the forefront as industry focuses on enhancing recovery from known reservoirs. With performance prediction for EOR processes calling for better numerical models of a reservoir rock and fluid properties, geostatistics is receiving renewed attention. Until recently, geostatistics was often associated with only one of its important contributions and was used as a synonym for kriging, a multiple regression technique that has been most commonly used as a gridding procedure. By reducing geostatistics to another canned gridding softwares, early users failed to realize its full potential as a set of spatial data analysis tools, as a probabilistic language to be shared by geologists, geophysicists and reservoir engineers, and as a vehicle for integrating various sources of uncertain information. Oversold as canned software with no regard for prior education, geostatistics generated overexpectations and the ensuing disappointment. Fortunately, recent experience has brought forward more reasonable expectations and has broadened the scope of applications. Spatial Data Analysis Geostatistics begins with an emphasis on describing and modelling the spatial variability of reservoir properties and the spatial correlation between related properties such as porosity and seismic velocity. These models can then be used in the construction of numerical models for a variety of purposes-interpolations for a property whose average is critically important, stochastic simulations for a property whose extremes are critically important. Whether one needs to transfer information from one reservoir to another, or between different units within the same reservoir, or whether one needs to transfer information from one discipline to another, a quantitative vehicle is necessary. Geostatistical models of spatial variability and dependence provide a quantitative summary of geological observations, and provide a quantitative summary of geological observations, and can therefore serve as a such a vehicle. P. 353

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