Detecting and Analysing Fractures in the Subsurface: The Critical Steps in Modeling Fractured Carbonate Reservoir

Abstract

Abstract In Abu Dhabi Offshore areas, the development of Lower Cretaceous fractures carbonate reservoir is a challenge that can be achieved using fracture characterization modelling approach. Generally, open fractures are part of the main factor controlling the permeability and connectivity of different reservoir flow units within the system; consequently the identification, description and classification of these fractures in such carbonates are essential to effective fracture modelling, formation evaluation, well placement and planning. Identifying fracture network properties require all available data to be synthesized for such predictive modelling. The first step to establish a reliable predictive model of fractures is to collect the proper data in a consistent and systematic manner from core, image logs, outcrops, rock mechanics and dynamic data including mud losses, flow meter logs, well tests and production data such as gross rates, initial PI and water cut. Since image logs are usually limited in number, it is necessary to use open hole logs to extrapolate findings to wells with no image logs. One of the main steps in fractured reservoir characterization is to interpolate fracture attributes between wells using seismic data. The next step is to identify the geologic controls (drivers) that have a great impact on the intensity and spatial distribution of fractures such as structure, thickness, lithology, composition and texture, faults and curvature. Nonlinear regression tools are used to rank and correlate the multitude of geological drivers. The selected drivers are used to predict the fracture intensity and generate field scale fracture attribute maps. The final step in fractured reservoir modelling is to generate a discrete fracture network (DFN) model which determines the number of fractures in each grid block based on the value of the fractures intensity and to output the parameters to a reservoir simulator and compare the simulation results with known well behaviour and field production data.