Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models

Abstract

This article describes the workflow used in continuous fracture modeling (CFM) and its successful application to several projects. Our CFM workflow consists of four basic steps: (1) interpreting key seismic horizons and generating prestack and poststack seismic attributes; (2) using these attributes along with log and core data to build seismically constrained geocellular models of lithology, porosity, water saturation, etc.; (3) combining the derived geocellular models with prestack and poststack seismic attributes and additional geomechanical models to derive high-resolution three-dimensional (3-D) fracture models; and (4) validating the 3-D fracture models in a dynamic reservoir simulator by testing their ability to match well performance. Our CFM workflow uses a neural network approach to integrate all of the available static and dynamic data. This results in a model that is better able to identify fractured areas and quantify their impact on well and reservoir flow behavior. This technique has been successfully applied in numerous sandstone and carbonate reservoirs to both understand reservoir behavior and determine where to drill additional wells. Three field case studies are used to illustrate the capabilities of the CFM approach.