MACDONALD, ALISTER C., and JAN OLE AASEN, Statoil Research Centre, Trondheim, Norway
ABSTRACT: Parameter Estimation for Stochastic Models of Fluvial Channel Reservoirs
Stochastic modeling techniques are being used increasingly to describe the geometries of fluvial channel reservoirs as a basis for important field development decisions such as optimal well configuration. One of the main problems associated with the application
of such tools is the inference of the necessary input parameters to describe the models. In particular, it is difficult to estimate the lateral continuity and stacking pattern of the sandstone reservoirs. This paper will focus on the application of outcrop data sets for inferring realistic input parameters. Input to two different stochastic models is described: one simple model (a binary truncated Gaussian field), and one more complex model (a hierarchical marked-point model with spatial interaction functions). Parameters have been estimated from outcrops in Spain and the United States, and the models have been tested on data from a North Sea oil field.
Indicator variograms estimated from outcrops can be used to define the spatial correlation structure of a truncated Gaussian field and control the continuity of sandstones simulated using such a model. Although indicator variograms are only two-point statistics, they provide a reasonable description of sandstone geometries. They are also intuitive for geologists, and correlation lengths reflect bed thicknesses and widths.
The hierarchical marked-point model comprises a population of "points" (lines in 3-D models), which describe the positions of channel belts. The spatial arrangement of the lines is partly controlled by interaction functions, which are used to define the (non-Poisson) distribution of the channel belts. Each channel belt is associated with a series of "marks," which define the channel dimensions, number of channels, and channel stacking. Parameter estimation in this model is more complex, but typical values of parameters such as channel dimensions, channel stacking patterns, and channel-belt stacking patterns have been estimated from outcrop data.
The study has illustrated that geostatistical analysis of outcrop analogs can be used to provide reasonable input parameters to stochastic models of fluvial reservoirs. Analog data are, however, never fully transportable to real field situations, and the full potential of stochastic modeling requires the integration of analog data with field-specific measurements, for example, well tests, which can be used to constrain further the model parameters.
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