Application of EM algorithms for seismic facices classification |
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Authors: | Mei Han Yong Zhao Gaoming Li Albert C Reynolds |
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Institution: | 1.University of Tulsa,Tulsa,USA;2.Houston,USA |
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Abstract: | Identification of the geological facies and their distribution from seismic and other available geological information is
important during the early stage of reservoir development (e.g. decision on initial well locations). Traditionally, this is
done by manually inspecting the signatures of the seismic attribute maps, which is very time-consuming. This paper proposes
an application of the Expectation-Maximization (EM) algorithm to automatically identify geological facies from seismic data.
While the properties within a certain geological facies are relatively homogeneous, the properties between geological facies
can be rather different. Assuming that noisy seismic data of a geological facies, which reflect rock properties, can be approximated
with a Gaussian distribution, the seismic data of a reservoir composed of several geological facies are samples from a Gaussian
mixture model. The mean of each Gaussian model represents the average value of the seismic data within each facies while the
variance gives the variation of the seismic data within a facies. The proportions in the Gaussian mixture model represent
the relative volumes of different facies in the reservoir. In this setting, the facies classification problem becomes a problem
of estimating the parameters defining the Gaussian mixture model. The EM algorithm has long been used to estimate Gaussian
mixture model parameters. As the standard EM algorithm does not consider spatial relationship among data, it can generate
spatially scattered seismic facies which is physically unrealistic. We improve the standard EM algorithm by adding a spatial
constraint to enhance spatial continuity of the estimated geological facies. By applying the EM algorithms to acoustic impedance
and Poisson’s ratio data for two synthetic examples, we are able to identify the facies distribution. |
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