Optimal parameter updating in assisted history matching of the Nelson Field using streamlines as a guide

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 data. 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 Neighbourhood Algorithm.

We apply these approaches to 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 history match to improve production rates.

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.

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 behaviour and have been able to better predict flow from simulation models.