Integrated Modeling of the Fractured Carbonate Midale Field and Sensitivity Analysis Through Experimental Design

Abstract

Summary We used an integrated solution by combining "direct" and "inverse" approaches to fracture network characterization in a stochastic numerical model. Static geological data obtained from cores and well logs were used together with dynamic data such as well-test responses to build 3D discrete fracture-network (DFN) models. We used the data obtained from the fractured carbonate Midale field in Canada. The fractured-reservoir model was constructed from static and dynamic (drawdown and pulse-interference tests) data. Matrix and several fracture parameters including fracture length, density/ spacing, aperture, connectivity, and orientation were evaluated in a quantitative sensitivity study to determine which characteristics have a higher influence on the accurate match to well-test response. We used experimental design to optimize the number of simulations needed for a sensitivity study and history match. The sensitivity analysis revealed a strong influence of matrix quality on the pressure response, suggesting that the history match can be specific to the simulated process and not necessarily unique. The results emphasize the contribution of matrix in the Midale reservoir and the need to simulate a broader range of processes for an accurate description of the fracture/matrix system dynamics. In a general sense, the approach used in this study proved to be useful in integrating fracture data from different sources and assessing its reliability and relative importance.