Abstract
The main objectives of this study are analysis of spatial behavior of the porosity and permeability, presenting direction of anisotropy for each variable and describing variation of these parameters in Shurijeh B gas reservoir in Khangiran gas field. Porosity well log data of 32 wells are available for performing this geostatistical analysis. A univariate statistical analysis is done on both porosity and permeability to provide a framework for geostatistical analysis and modeling. For spatial analysis of these parameters, the experimental semivariogram of each variable in different direction as well as their variogram map plotted to find out the direction of anisotropy and their geostatistical parameters such as range, sill, and nugget effect for later geostatistical work and finally for geostatistical modeling, two approaches kriging and Sequential Gaussian Simulation are used to get porosity and permeability maps through the entire reservoir. All of statistical and geostatistical analysis has been done using GSLIB and PETREL software. Maximum and minimum direction of continuity are found to be N75W and N15E, respectively. Geostatistical parameters of calculated semivariogram in this direction like range of 7000 m and nugget of 0.2 are used for modeling. Both kriging and SGS method used for modeling but kriging tends to smooth out estimates but on the other hand SGS method tends to show up details. Cross-validation also used to validate the generated modeling.
Highlights
Geostatistics is a branch of applied statistics that deals with spatially correlated data based on the theory of the regionalized variables
The goal of this study is to characterize the distributions of porosity and permeability in Shurijeh-B reservoir of Khangiran gas field
All of data that are related to zone B of Shurijeh formation are separated and extracted from all available porosity well log data and after doing this, we find permeability values by using both formula which we have found in last section
Summary
Geostatistics is a branch of applied statistics that deals with spatially correlated data based on the theory of the regionalized variables. It was initially addressed by George Matheron of the Centre de Morophologie Mathematicque in Fontainebleau, France in 1960s. The geostatistical models can provide interesting solutions to the two important challenges These include: the construction of 3-D geologically realistic representations of heterogeneity and the quantification of uncertainty through the generation of variety of possible models (or realizations). In the early Eighties, Issaks and Srivastava (1989) renewed the interest in the approach used at Hass-Messaoud Later this was further perused by Haldorsen and Davis (1987) that lead with the fast progress in computing facilities, to generate the models in three dimensions. Later this was further perused by Haldorsen and Davis (1987) that lead with the fast progress in computing facilities, to generate the models in three dimensions. Dowd (1994) emphasized the importance of using the nonlinear geostatistics in case of non-normally distributed data
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