Facies Modeling of Heterogeneous Carbonates Reservoirs by Multiple Point Geostatistics

Abstract

Facies modeling is an essential part of reservoir characterization. The connectivity of facies model is very critical for the dynamic modeling of reservoirs. Carbonate reservoirs are so heterogeneous that variogram-based methods like sequential indicator simulation are not very useful for facies modeling. In this paper, multiple point geostatistics (MPS) is used for facies modeling in one of the oil fields in the southwest of Iran. MPS uses spatial correlation of multiple points at the same time to characterize the relationships between the facies. A small part of the oil field, in the vicinity of the simulation grid, is used as a training image, in which there is 25 well data for creating suitable training image by the principal component analysis (PCA) method. In this study, MPS is successfully applied to facies modeling and the spatial continuity of facies is reasonably reproduced. The facies model verifies the reproduction of facies proportion in training image and wells. Also, five wells are used for the cross correlation of the facies model. The results indicate that the facies model shows a strong correlation with the facies of these five wells. Additional hard data, which is extracted from high confidence seismic data, is so useful for the improvement of the facies model.

Keywords

• facies
• seismic
• data conditioning
• pca
• mps