Summary
Ensemble methods have been applied successfully in assisted history matching
and in production optimization. In history matching, the ensemble Kalman filter
(EnKF) has been used to estimate the values of hundreds of thousands of
variables from various types of data. In production optimization, an
ensemble-based method has been used to estimate optimal control settings for
problems with thousands of control variables. In both cases, relatively small
numbers of random realizations are used to compute update directions for
improving estimates.
In this paper, we illustrate the application of the ensemble-based
optimization on two fairly complex problems that would be difficult to handle
by other methods. In the first example, we show its application to optimize
inflow-control-valve (ICV) settings on two horizontal wells in a sector model
of 200,000 cells. One hundred layers were used in the reservoir model to
capture geological heterogeneity. The two wells were drilled parallel to the
edgewater boundary. The optimization objective in this example is to minimize
cumulative water production over a 10-year production period while maintaining
a constant liquid-production rate. Results after only five optimization
iterations with improved control-valve settings showed a 50% reduction in
cumulative water production. The fully automated optimization process was
completed within a few hours under a parallel-computing environment.
The ensemble-based method was also applied successfully to a 3D case
consisting of 10 multilateral wells with ICVs installed at each lateral
junction. The interaction of various laterals is difficult to visualize, but
the optimization algorithm was again successful in reducing water production.
In this example, we demonstrate that proper choice of control variables can be
important to the success of the optimization.
© 2010. Society of Petroleum Engineers
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History
- Original manuscript received:
30 August 2009
- Meeting paper published:
10 May 2009
- Revised manuscript received:
12 December 2009
- Manuscript approved:
13 January 2010
- Published online:
23 August 2010
- Version of record:
9 December 2010
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