A probabilistic approach to integrating dynamic data in reservoir models

Developing geostatistical reservoir models that are geologically realistic and correctly reflect production history is important for accurately assessing the uncertainty associated with production forecasts. Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability, etc.). The focus of this paper is to present a methodology for efficiently integrating dynamic production data into reservoir models. In contrast to other methods for production data integration, the proposed methodology attempts to quantify the information in production data pertaining to reservoir heterogeneity in a probabilistic manner. The conditional probability representing the uncertainty in permeability at a location is iteratively updated to account for the additional information contained in the dynamic response data. It is demonstrated that the reservoir models obtained by integrating dynamic data exhibit accurate reservoir connectivity characteristics. A localized perturbation procedure is also presented to account for multiple flow regions within the reservoir. Such an improved scheme utilizes a set of locally varying deformation parameters to guide the iterative updating process in order to obtain a global history match. The proposed methodology is demonstrated on a realistic case example.