Fractured Reservoir Characterization Using Streamline-Based Inverse Modeling and Artificial Intelligence Tools

Abstract A new approach that combines the use of static and dynamic data in fracture modeling has been developed. The approach provides the unique opportunity to constrain the fractured models to an effective permeability derived by automatically matching well performances using a streamline-based inverse approach. The effective permeability derived from well performances reflects the stress changes induced by fluid injection, and takes into account the increase of fracture conductivity along the present day maximum horizontal stress. Given the simplicity, robustness, and speed of the streamline based inversion, the estimation of effective permeability is proposed as an alternative to the tedious and often unreliable process of fracture modeling using stress measurements. Because of the availability of a true integrated fracture modeling framework, where any type of data could be used when deriving fracture models, the use of effective permeability derived by inversion is able to reduce the uncertainties by providing models with better fracture prediction capability, as illustrated on a carbonate reservoir.