Optimal parameter updating in assisted history matching 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 property values can be difficult. In practice, not all are important but finding which parts of the reservoir require updating is a challenge. In this work, we investigate methods of history matching by focusing on sub-sets of the full array model parameters 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 (North Sea, UK) 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 modifying the properties all around the well.