Streamline based reservoir screening for improved automatic production and seismic history matching

It is becoming more and more common to use assisted history matching methods to find different combinations of reservoir simulation models that agree with production and time lapse seismic data if exists. Models with a large number of cells contain millions of unknown parameters and selecting the correct values can be difficult. In practice not all are important but finding which parts of the reservoir require updating can be difficult. In this work we investigate methods of history matching by focusing on sub-volumes of the parameter space and we use streamlines to help us choose where the model requires change. We identify localities in the reservoir that affect particular wells and we update reservoir properties (net:gross and permeability) within. We control changes using the pilot point method combined with a Neighborhood Algorithm.We first apply these approaches for production history matching of the Nelson field where uncertainty of the shale distribution controls predictions. The field is divided into localities based on the performance of the worst well predictions. We then extend the application to a more complex case including both production and seismic data in history matching. The localities that require change are sufficiently separate that we can modify them one at a time. We also compare our result with a more ad hoc approach where the whole area around the well is modified. We find that, for the wells of interest, the streamline guided approach gives a 70% improvement in the history match from our starting model and around 40% reduction of misfit in prediction. This improvement is twice that of the total area approach. The streamline approach has been applied successfully for seismic and production history matching of Nelson that makes 50% and 35% improvement of well production in history and prediction periods and 10% improvement of seismic map compared to the base simulation model.Reservoir simulation and history matching are crucial for reservoir management especially when developing field management plans. By application of a good updating workflow in the right area of the model through an automatic history matching process we have gained a greater insight into reservoir behavior and have been able to better predict flow from simulation models.