Integration of Production Data for Estimation of Natural Fracture Properties in Tight Gas Reservoirs Using Ensemble Kalman Filter

Abstract

Abstract Productivity in deep-basin tight gas reservoirs can be improved significantly by natural fracture enhanced permeability. Therefore, deviated and horizontal wells are often drilled to intersect highly fractured formations. Unfortunately, fractured reservoirs are highly heterogeneous, often characterized by probability distributions of fracture properties in a discrete fracture network (DFN) model. In addition, the relationship between recovery response and model parameters is vastly non-linear, rendering the process of conditioning reservoir models to both static and dynamic (production) data challenging. In the current paper, a novel approach is presented for uncertainty assessment and characterization of fractured reservoir model parameters using data from diverse sources. First, Monte Carlo based techniques were used to generate multiple DFN models conditioned to geological and tectonic information, accounting for the uncertainty associated with static data. Next, each model or realization was upscaled for flow simulation. Finally, Ensemble Kalman Filter (EnKF), a data assimilation technique that has been used for assisted history matching, was employed to update the DFN models using production data. In order to ensure positive definiteness of the updated permeability tensors, to reduce the size of model parameter space, and to eliminate the redundancy between parameters for improved convergence, principal component analysis was performed such that only the main principal components of the full permeability tensor and sigma factors were updated through EnKF algorithm. The qualities of the history-matched models were assessed by comparing the spatial distribution of the updated model parameters with the initial ensemble, as well as the Root Mean Square Error (RMSE) of the predicted data mismatch. The results clearly demonstrate that, characterization of fractured reservoirs combining DFN modeling with updating principal components of the upscaled model parameters through EnKF has the potential to resolve the shortfall of traditional techniques for history matching of such complicated reservoirs. The proposed approach can be used effectively to update reservoir models and optimize development plans in unconventional gas reservoirs using continuous flow and pressure measurements.