Print this pageAbstract: Object-Based Stochastic
Modeling
of Turbidite Reservoirs Geometry and Heterogeneities
Lanzarini, W.; A. Barreto Jr.; and M. dos Santos - Petrobras/Cenpes; G. Tavares; H. Lopes; and S. Pesco - PUC-Rio
The combination of stochastic and geometric
methods in the object-based approach has proved an adequate alternative
to generate equiprobable realizations of the reservoir architecture. In
the current work, object-based modeling and stochastic simulation is used
in order to create 3-D stratigraphic domains of turbidite reservoirs.
The Stochastic Modeling of Geologic Objects (SMGO)
method used in this work is a process for conditional and non-conditional
distribution of geologic objects in a reservoir domain. It is composed
of three main steps: parameterization, geometric modeling and stochastic
simulation of geologic objects.
The parameterization and geometric modeling of
channel (Fig. 1), lobe and transitional channel-lobe genetic units for
turbidite depositional system is performed. The parameter dimensions and
correlation functions are obtained from modern turbidite systems, analogous
outcrops, similar reservoirs and from 3-D seismic interpretations.
The geometric modeling stresses topological relations
on a primary level, leaving the geometrical and physical attributes to
be placed on the topological structures. The topology of discrete objects
is given by unambiguous relationships between its vertices, edges and faces.
The simulation process starts from random points
(object germs) in the space. The defining parameters of each turbidite
object, such as length, width, thickness sinuosity and direction, are the
input data to the simulator, and can be edited by the user as absolute
values, as probability distributions or as correlation functions between
the parameters.
The stochastic simulation ends, when: global facies
proportion is reached (non-conditional simulation), reservoir intervals
in the conditioning wells are honored (conditional to wells), and vertical
facies proportion curve is fitted (conditional to proportion curve).
Once the simulation results are available (Fig.
2), domain points can be characterized as internal or external to the simulated
objects for the sake of further attribution of reservoir properties (porosity
and permeability), and reservoir volumes can be defined. Statistics and
data analysis of simulated objects' parameters and reservoir architecture
allows the development of uncertainty studies for reservoir
management.
AAPG Search and Discovery Article #90933©1998 ABGP/AAPG International Conference and Exhibition, Rio de Janeiro, Brazil