Reservoir facies modeling using stochastic inversion and probability perturbation method

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Abstract

One of the basic steps in determination of the characteristics of a reservoir is modeling its various facies. In this paper, a geostatistical inversion method is presented for facies modeling using well logs and angle stack data. First, the conditional probability of occurrence of facies conditioned to well logs in each cell has been calculated using the sequential indicator simulation method. Then, petrophysical and elastic properties of reservoir facies have been obtained using the Gaussian sequential simulation method and rock physics relations. In order to generate and update the facies model consistent with seismic data, the probability perturbation optimization algorithm has been used. This method tries to create a model of facies and other properties of the reservoir that have a good consistency with seismic data by successively changing the facies probability conditioned to seismic data in each cell. To obtain the total probability distribution of facies occurrence from the probability of facies conditioned to seismic data and the probability of facies conditioned to well logs, Tau model has been used. At each stage, after obtaining different properties, a geophysical forward model is constructed and compared with seismic data. Finally, all these steps are used for different possible models obtained from the sequential indicator simulation method. This method is applied to synthetic data sets with different signal-to-noise ratios. In the case of seismic data with a signal-to-noise ratio of 9, a high-resolution model for the facies has been obtained that is 81.83% consistent with the reference facies model and has improved the initial facies model by 19.97%. In order to further investigate this method, it has also been applied to seismic data with the signal-to-noise ratios of 4 and 2. The results have shown that this method has a good ability to detect facies and other petrophysical and elastic properties of the layers in the reservoir.

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References

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Volume 7, Issue 3
January 0
Pages 279-297

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