Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence

Abstract

Abstract A new approach in fractured reservoir characterization and simulation that integrates geomechanics, geology, and reservoir engineering is proposed and illustrated with actual oil reservoirs. This approach uses a neural network to find the relationship between, reservoir structure, bed thickness and the well performance used as an indicator of fracture intensity. Once the relation established, the neural network can be used to forecast primary production, or for mapping the reservoir fracture intensity. The resulting fracture intensity distribution can be used to represent the subsurface fracture network. Using the fracture intensity map and fracture network, directional fracture permeabilities and fracture pore volume can be estimated via a history matching process where only two parameters are adjusted.