Quantification of Statistical Geological Parameters from Genetic Modeling of Channels
Authors: Doligez Brigitte , Versluys Virginie , Lopez Simon , Teles Vanessa , Lerat Olivier , Joseph Philippe , Euzen Tristan 
Published: February 2016

Abstract
Stochastic simulations can describe complex geological architecture, spatial facies distribution, and internal variations of reservoir properties. The simulated models are conditioned to available data but still present a significant amount of uncertainty in interwell areas. Genetic models are now emerging approaches allowing a better identification and understanding of key physical parameters and providing possibly a better prediction of reservoir architecture.
We present a new methodology for modeling sedimentary structures based on the combined use of genetic and stochastic models. The parameters of the genetic model are quantified from actual outcrop data through a thorough analysis of the key parameters. In a second step, statistical parameters such as facies proportion distributions are quantified from the obtained images and used in a more classical geostatistical model, based on the truncated gaussian simulation method. The results of this second modeling is finally compared to the reality.
This coupled approach is applied to the Mesa Verde Menefee floodplain as an outcrop example of a meandering channel system. The final results show a good control of the geostatistical simulation by the genetic model. The method allows to generate more realistic geological heterogeneities distribution than using a purely stochastic approach.

Introduction
The importance of geological model for reservoir characterization is nowadays acknowledged, as well as the significant effects of the geological framework on the characteristics of the reservoir models generated [1].
Modeling sedimentary heterogeneities at reservoir scale is classically performed using geostatistical methods. Geostatistics reproduce the spatial distribution of heterogeneities characterized from available data. Moreover they can constrain the simulations with an easy integration of secondary data such as seismic profiles usually at a different resolution scale. They are several geostatistical methods to build 3D reservoir model simulations, such as the object-based or variogram-based methods [2]. Object based methods generate objects with predefined shapes which are distributed randomly in the simulation space. Variogram-based methods use statistical tools to characterize the spatial structure of the subsurface available data and then simulate equi-probable structures with the same spatial characteristics. None of these geostatistical methods is able to reproduce realistic and complex stratigraphic heterogeneity patterns, which can be major controlling features for fluid flow or spatial variations of acoustic properties.
Nowadays, genetic models which embeds the modeling of sedimentary processes responsible for these heterogeneities are emerging with promising fields of applications. These models can reconstruct different and very realistic sedimentary architectures. These resulting images can be compared to field data which allows the identification of key parameters and the improvement of the conceptual models themselves. Moreover from these images statistical parameters can be extracted to be used in geostatistical methods which can consider additional geological constraints.
Process based sedimentology is beginning to provide inputs into geological model building. These methods have the advantage to generate realistic images of the geology, but they are difficult to constrain them to well data. They can also be highly time- consuming.