Print this pageComparison of Geostatistical Approaches for Diagenesis Quantification
Diagenesis changes the original relationship between the facies and the distribution of the petrophysical properties. The objective of this work is to compare several methods to integrate diagenetic constraints into geostatistical models using new methodological workflows. The main objective is to define rules and to acquire know-how in order to recommend one methodology or another, depending on the geological environment, the context of diagenesis, and the available data.
On a real dataset in fluvial environment, a relationship has been quantified between the pore chlorite filling and the grain size, which allowed to define several degrees of diagenesis in the reservoir. Several alternative methods have been used to create a geological model of this reservoir with properties in terms of sedimentary facies and diagenetic class, as nested simulations, plurigaussian simulations and bi-plurigaussian simulations.
In the nested simulations workflow, each step is done sequentially: simulation of sedimentary facies, simulations of the classes of diagenesis within each sedimentary facies, then reconstitution of the final model.
The simulation parameters are specific for each property, sedimentary facies and class of diagenesis. This method allows to uncouple the constraints in terms of conditioning wells and parameters, for each simulated property. In return, it will not be possible to use a correlation between sedimentation and diagenesis.
The direct plurigaussian simulation (PGS) method has several strong points: a unique simulation is launched globally and a correlation coefficient may be used between the two Gaussian functions, which could translate the relationship between sedimentation and diagenesis. But the method is applied to only one global variable, and will be constrained only with the wells owning the two types of information, sedimentary facies and diagenesis.
A new model, the Bi-PGS model has been developed that provides a sound basis for bivariate categorical simulation. It is flexible as each physical process is associated with a complete PGS (possibly using two underlying Gaussian random function). A further possible development is to introduce a correlation between the two PGS. The Bi-PGS technique can cope with non-stationarity by using proportions for each sedimentation and diagenetic classes that vary in space. It would also be possible to use the Bi-PGS technique for processing two categorical data sets of different qualities.
AAPG Search and Discovery Article #90090©2009 AAPG Annual Convention and Exhibition, Denver, Colorado, June 7-10, 2009