Fractured Basement Characterization from Multi-Attributes Guided Integrated Continuous Fracture Modeling and Discrete Fracture Network Modeling

This paper demonstrates an integrated approach to model conditioning for fractured basement reservoirs through application of Continuous Fracture Modeling (CFM) and Discrete Fracture Network modeling (DFN). The approach has been implemented into an advanced software system, and is built on four main steps: 1) the interpretation and analysis of high resolution borehole images, sonic data (Stoneley and shear), log and core data which provide high vertical resolution information for a limited number of locations, and 2) the prediction of the fracture intensity in the inter-well space, 3) the generation of the DFN model, and 4) the DFN upscaling. The process involves identifying the flow contributing fractures using a detailed analysis of borehole images data and then combining them with Sonic measurement and production data. An optimized set of key seismic attributes is used to constrain the propagation of fracture intensity away from the wells: ‘fracture sensitive’ attributes such as frequency attenuation and results of full stack inversion, and texture seismic attributes derived from poststack signal processing. Fracture models are then contructed using first neural network artificial intelligence method, and secondly discrete fracture network. The robustess of the method is based on both qualitative and quantitative analysis of the data at each step of the workflow.