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.