Deterministic Versus Stochastic Discrete Fracture Network (Dfn) Modeling, Application in a Heterogeneous Tight Gas Reservoir

Meyssam Tavakkoli¹, Saeed Khajoee², Reza Malakooti³, and Mohsen Safari Beidokhti^4
1Petroleum Exploration Engineering (IFP SCHOOL-PUT), Paris, France.
²IFP SCHOOL-PUT, Paris, France.
³Calgary University, Calgary, ON, Canada.
⁴Curtin University, Perth, WA, Australia.

Fracture Modeling is a multi-step process involving several disciplines within reservoir characterization and simulation. The main idea is to build on geological concepts and gathered data such as interpretation of beds, faults and fractures from image log data, use field outcrop studies as analogs for conceptual models, seismic attributes used as fracture drivers, etc. The purpose of modeling fractures is to create simulation properties with the power to predict the reservoir behavior. By modeling the fractures explicitly, one can honor the spatial relationships between properties in adjacent cells.

A Discrete fracture network is a group of planes representing fractures. Fractures of the same type that are generated at the same time are grouped into a fracture set. Each fracture network containing fractures has at least one fracture set but may have many.

The simplest fracture sets are defined deterministically as a group of previously defined fractures, either as a result of fault plane extraction from a seismic cube, or as previously defined fractures.

Fractures modeled stochastically can be described statistically either using numerical input or properties in the 3D grid. Properties in the 3D grid can vary in 3D and can easily be modeled using seismic attributes from 3D seismic data.

The Scale up fracture network converts the discrete fracture network (with its defined properties) into the properties that are essential for running a dual porosity, or dual permeability simulation.

A simple simulation model is developed for three different grid types, using the software ECLIPSE 100, grid without DFN modeling, deterministic DFN modeling and stochastic DFN modeling. The results of the reservoir simulation indicate that case with Stochastic DFN has a better result (history match) rather than cases with Deterministic DFN and the grid without DFN.

AAPG Search and Discover Article #90096©2009 AAPG 3-P Arctic Conference and Exhibition, Moscow, Russia