Technical Paper: Workaround to Build Robust Facies Model with Limited Input Data; a Case Study from North West Kuwait

Society: EAGE
Paper Number: Tu-P07-02
Presentation Date: 2013


The objective of this study is to build a reliable 3D facies model that can describe facies architecture across the field and can delineate reservoir, and at the same time will be utilized to distribute porosity. Five litho-facies were described from detailed core study in two wells. These litho-facies have been propagated to non-cored intervals in other wells utilizing a supervised Neural Network technique.

The issue at hand was the development of variogram with limited well data and its subsequent application in the stochastic algorithm with reduced uncertainty. Lack of data prevents conventional way to generate facies variogram. The facies trend maps that show the lateral variation of facies have been developed using various inputs such as paleo shoreface trends, sequence stratigraphic zones and facies fraction. The facies maps were constructed from facies fractions using trend maps. Subsequently variogram maps were built from normalized facies maps. Variogram parameters i.e. major, minor range and azimuth are inferred from variogram maps. These parameters are in close proximity when compared to regional facies trends. Analyzed variogram parameters were used to build the facies model.

To validate this workflow a blind well test was run on one well and 65% success has been achieved at the well location and 82% over the whole model. These success rates are considered to be significant considering geographically wide study area and limited number of input wells. The produced model has been used to understand the lateral and vertical distribution of facies in the study area and is used successfully to distribute porosity across the field.

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