Abstract: 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