The installation of intelligent well completions is often justified by the technology’s ability to manage zonal inflow remotely with the consequent reduction in the well intervention frequency. A range of well and field production optimisation techniques that screen all possible zonal flow-management actions have been developed to control and optimise such intelligent wells and fields. This paper sets out to explore whether such techniques can be extended to provide an optimised work-over schedule for an oil field developed with conventional wells.

Published work-over schedule methods typically rely on well screening and ranking algorithms that are based on the current production performance of the well(s) being considered. This approach, which reacts to the current production situation, may lead to sub-optimal solutions due to the omission of work-overs which may not be the highest ranked at the time of implementation, but could prove to be more profitable in the longer term.

This study develops a workflow to design a proactive, work-over optimisation workflow using Genetic Algorithms (GA). It is based on an integrated, economic simulation model of the field which includes both the direct and the indirect costs associated with well interventions. The paper explores whether a "steered" GA created by the addition of screening and advanced sampling methods to a "pure" GA provides sufficient simplification of the problem to make it suitable for routine use in the field. Problem formulation and variable definition is discussed, allowing the inclusion of field knowledge to reduce the search space and steer the optimiser. The advantages of this modified GA are quantified by comparing it with the performance of the "pure" GA optimisation approach to the problem.

A significant added field value was achieved by the "steered" GA when compared with the "pure" GA.

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