Calibration of Fractured Reservoirs with Dynamic Data

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractFractured reservoirs are reservoirs whose productivity and performance are controlled or strongly affected by the fracture network occurring in the field. Therefore, a correct characterization of the reservoir internal geometry and a suitable description of the effective fracture network are crucial for a predictive model to be reliably applied to performance prediction and production forecast. A new approach has been attempted to validate the reconstructed internal geometry of a selected fractured reservoir by reproducing the dynamic behavior of the reservoir monitored during MDT tests. The description of the reservoir fracture network was achieved by integration of relevant data that could be collected from wireline logs, especially imaging and sonic logs, conventional cores, specifically aimed at microfracture examination, small drilling mud loss analysis, and field scale observations of regional structural heterogeneities from outcrop analogs inspection. Fracture types, distribution, geometry, properties, interaction with matrix, and deformation history were thus defined. Fracture patterns were described in terms of distributions for orientation, size, shape, spatial location and intensity to stochastically generate a static model of the fractured reservoir. The dynamic behavior of the fractured system was reproduced by a finite element flow model able to simulate transient flow through three-dimensional rock masses with discrete fracture networks. Consistency was required between the observed and simulated pressure data also in terms of derivative pressure trend to assure that the reservoir geometry was adequately modeled. Analysis of the model response as a function of the assigned fracture parameters and comparison between the observed and simulated dynamic behavior allowed calibration of the fracture modeling parameters and achievement of a satisfactory description of the reservoir effective fracture network.