A new method for reservoir characterization based on fractal and geostatistics methods

The proper characterization of reservoir heterogeneities plays an important role in reservoir evaluation and forcasting of its performance. However, the heterogeneity of a reservoir and its property distributions in the large scale of reservoir volume could not be properly determined and estimated with known data from a few wells. In such a case, one of the newest and roboust methods in reservoir characterization is the use of fractal methods whenever the geostatistics fails. The objective of this paper is to introduce and discuss a new method that is the combination of fractal geometry (small scale) and geostatistics (large scale) to characterize the reservoir property distributions in a gian reservoir with a few known well data. The developed method was applied to a real reservoir in the south west of Iran with three exploration well data. In this method, first of all, the reservoir zonation and formation units including carbonate, sandstone and shale must be identified from the petrophysical data by using multivariate statistics analysis. So that, the porosity, permeability, and water saturation for the known wells could be estimated from the core and log data. Then, spatial correlation using variogram analysis and quantify heterogeneities using R/S analysis, can be determined for each zone in the wells. Eventually, merging kriging method and fGn noise of fractal will lead to develope our new method to estimates and predict the reservoir property distributions between wells and throughout the reservoir more accurate and very precise than the previouse methods. The produced error is in acceptable and accurate range that was less than 4.93 percent for porosity for instance. The output of the model may be used as an input for geomodeling and simulation softwares. Also, heterogeneity distributions of reservoir characteristics are well visualized by fractal distributions.