| Popis: |
We consider the problem of finding optimal long-term (life-cycle) recovery strategies for hydrocarbon reservoirs by use of simulation models. In such problems the presence of operating constraints, such as for example a maximum rate limit for a group of wells, may strongly influence the range of possible solutions. Commercial simulators often offer the possibility to use heuristic rules to deal with constraints during simulation. It is first shown that such an approach imposes serious limitations for important cases of interest, such as finding optimal operating strategies for smart wells. We subsequently consider the use of gradient-based numerical optimization approaches, which are considered the most efficient for realistically complex reservoir cases. Formal treatment of especially output constraints in such approaches is challenging and is usually associated with high computational costs or strong simplifications. We propose and demonstrate a method that addresses these challenges by efficient use of an ensemble to approximate constraint gradients with respect to the well controls. The gradients are then used by an optimization algorithm to maximize an objective function, typically net present value, while avoiding violation of the constraints. The method was implemented in an in-house optimization framework and tested on two example cases. The first example case is a relatively simple homogeneous 2D reservoir with four injection wells and one producing well, all equipped with adjustable valves and operating at fixed pressures. We consider the presence of a maximum field water injection rate constraint, as well as individual well rate constraints. The method is also applied to a realistically complex 3D case with 30 smart wells equipped with ICVs, modified from the original Brugge benchmark model. The results show that in both example cases the method is able to find improved recovery strategies that do not violate the constraints. It is also shown that proper choices for constraint grouping, balancing of objective function and constraints, and of the initial control strategy may positively impact the efficiency and effectiveness of the optimization process. The proposed method is an improvement to constraint handling by simulators for smart well optimization and enables application of optimization workflows to field cases with realistic operating constraints. |
