A Discrete Fracture Network Approach to Conditioning Petroleum Reservoir Models Using Seismic Anisotropy and Production Dynamic Data

Abstract

Abstract This paper demonstrates an integrated approach to conditioning models for fractured petroleum reservoirs, through application of discrete fracture network (DFN) methods. The approach is built on the observation that discrete fractures controlling reservoir production can also influence the anisotropy of seismic response. The paper extends previous work by the above authors by considering realistic fractured reservoir geometries including multiple fracture sets. The presence of a system of natural fractures in a reservoir induces anisotropy in its hydraulic and elastic properties. Fracture induced hydraulic anisotropy and related heterogeneous connectivity is evidenced through systematic non-uniform production performance. Elastic anisotropy may be observed in seismic data as azimuthally dependent elastic attributes such as compressional wave velocity and reflection amplitude versus azimuth which may be inverted from 3D seismic data. Previous work by the above authors demonstrated a method for simultaneous inversion of production and seismic observations through a gradient-based optimization scheme. In that work, the improvement in conditioning of the DFN model was demonstrated in a synthetic case containing a single fracture set. The current work builds on previous work by the authors through application of the new method to reservoirs containing two fracture systems. The robustness of the method with respect to host rock type is tested through use of matrix petrophysical and elastic properties representative of typical fractured reservoir lithologies. DFN model preconditioning requirements are explored through sensitivity testing with respect to hydraulic, elastic, and geometrical model parameters. The added value of seismic anisotropy in the inversion is demonstrated through comparison with a similar inversion process using only production data in the objective function. The results show that the new method for integration of production and seismic anisotropy provides improvement in resolution of the geometrical properties of multiple fracture systems over that which is achievable using production observations alone. The speed and stability of model convergence using the new process is dependent upon both fracture network and host rock properties. Key issues in model preconditioning observed in sensitivity tests are discussed. Introduction It is known from theory and practice that both the permeability and the elastic response of fractured reservoirs exhibit anisotropic behavior that is strongly correlated to the characteristics of the fracture system. Historically, geophysical and reservoir engineering techniques for characterization of systems of natural fractures have been developed and pursued independently. Seismic anisotropy, which is predicted by elastic theory, has been used by several authors1,2,3,4,5 to estimate fracture orientation and density from 3D seismic datasets. Sophisticated reservoir engineering techniques have been developed for characterization of the hydraulic response of naturally fractured reservoirs6,7,8,9. More recently, Parney and LaPointe10,11 used stochastic discrete feature network (DFN) modeling techniques to forward model both the elastic and hydraulic response of a common fracture system. However, this work did not solve the inverse problem. Previous work by Will et al.12 demonstrated a novel method of solving the inverse problem for fracture system geometrical characteristics. This method, based on DFN modeling techniques, simultaneously employed both the reservoir having a single fracture set. An initial estimate of fracture system parameters, P32 intensity and fracture trend, were refined through an iterative least squares optimization technique in which the objective function contained both elastic and hydraulic parameters. Stochastic DFN modeling techniques were used for forward modeling of both elastic and hydraulic responses of the fracture set at each iteration using appropriate effective media models. The method was evaluated by comparing optimizations carried out using a) seismic and hydraulic data, against b) hydraulic data alone. The inversion including both seismic and hydraulic data produced significantly better results, with faster and more stable convergence of both P32 intensity and fracture set orientation.