In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (nonlinear) constraints (e.g., recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools.

We rely on robust (derivative-free) optimization combined with the filter method for nonlinear constraints. It should be noted that the approach yields not only a feasible optimized solution but also a set of alternative infeasible solutions that could be considered in case the constraints can be relaxed. The whole procedure is accelerated using streamline-derived information. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation).

The methodology is showcased using a real field in West Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor (RF). The optimization variables represent a discrete time series for well bottomhole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g., Generalized Pattern Search) and global search procedures (e.g., Particle Swarm Optimization). We provide solutions with different levels of approximation and computational efficiency. Without the acceleration achieved through streamline-derived information, the method, while effective, could be prohibitive in many practical scenarios. It is worthwhile noting that part of the solution determined in this work has been tested out on the real field.

Optimal management of brownfields is typically addressed using bottomhole pressure values or rates as well control variables. Well controls given as bottomhole pressure values, although not directly implementable in the real field, are often much easier to put into practice than if they are given as rates. However, optimization algorithms that deal with well rates as control variables can be in many cases computationally faster than methods based on bottomhole pressure values. In this work we combine the two aforementioned desirable features for the optimal management of mature fields: well controls are given as bottomhole pressure values for a more practical implementation, and these values are also determined efficiently using concepts borrowed from optimization via well rates.


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