Integrated Static and Dynamic Modelling Workflow for Improved History Matching and Uncertainty Modelling

Abstract

Abstract Traditionally, the history matching process is done only on the dynamic model, without any direct update to the geological (or static) model. As a result, geological uncertainties are not fully evaluated in the dynamic model. Non-integration of static and dynamic modelling results in either too much time being spent modelling detailed geological phenomena that have little impact on the dynamic behaviour of the reservoir, or, conversely, important geological and petrophysical parameters being misrepresented or missed out which may have significant impacts on the overall field development strategy. Ideally, if any updates to static parameters are required as result of history matching in the dynamic model, these changes should be reflected directly in the static reservoir model, thereby ensuring consistency between the static and dynamic models. In this paper, a workflow is presented where both the static and dynamic modelling software packages are integrated as part of the history matching process. This workflow involves input parameters being adjusted in the geological model directly. Uncertainty analysis tools are used to obtain multiple history-matched models, which results in an order of magnitude increase in speed compared to traditional history-matching processes. Not only will this methodology result in improved history-matched models with a wider range of production forecasts being captured, but more importantly, it will result in better understanding of the static and dynamic uncertainties and their interdependencies, leading to a more informed decision-making process with regards to overall field development. In addition, this methodology offers a platform where the subsurface professionals involved in reservoir model construction and simulation processes can focus their efforts on improving reservoir characterization and identify areas that require further data acquisition or improvement. This paper also describes how the workflow was successfully applied to a recently developed, producing and waterflooded oil field in South East Asia, and eventually delivering an optimized reservoir model for reservoir management and a probabilistic approach to production forecasting.