New Approaches for Generally Constrained Production Optimization with an Emphasis on Derivative-free Techniques

New Approaches for Generally Constrained Production Optimization with an Emphasis on Derivative-free Techniques

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Production optimization involves the determination of optimum well controls to maximize an objective function such as cumulative oil production or net present value. In practice, the satisfaction of general physical and economic constraints is also required, which typically results in optimization problems that are nonlinearly constrained. Examples of nonlinear constraints include maximum water cut and minimum oil rate for wells operating under bottomhole pressure control. In this paper we present and apply optimization strategies that are able to incorporate a large variety of general constraints. We have identified a promising approach in the filter method. This recently introduced methodology borrows concepts from multi-objective optimization and avoids many of the issues that arise when objective function and constraints are lumped together by a penalty function. In terms of the underlying optimization procedure, our focus here is on techniques that are not simulator invasive; i.e., they view the flow model as a black box. Along these lines, we study derivative-free methodologies such as generalized pattern search and Hooke-Jeeves direct search, in combination with nonlinear constraint handling techniques.
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Production optimization involves the determination of optimum well controls to maximize an objective function such as cumulative oil production or net present value. In practice, the satisfaction of general physical and economic constraints is also required, which typically results in optimization problems that are nonlinearly constrained. Examples of nonlinear constraints include maximum water cut and minimum oil rate for wells operating under bottomhole pressure control.

 In this paper we present and apply optimization strategies that are able to incorporate a large variety of general constraints. We have identified a promising approach in the filter method. This recently introduced methodology borrows concepts from multi-objective optimization and avoids many of the issues that arise when objective function and constraints are lumped together by a penalty function. In terms of the underlying optimization procedure, our focus here is on techniques that are not simulator invasive; i.e., they view the flow model as a black box. Along these lines, we study derivative-free methodologies such as generalized pattern search and Hooke-Jeeves direct search, in combination with nonlinear constraint handling techniques.

 The performance of the algorithms is demonstrated on two challenging generally-constrained production optimization problems where up to 25 wells are considered.

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