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|Title:||On Optimizing Maintenance and Production Scheduling of Oil Wells||Authors:||Shekhah Alhaleela||Supervisor:||Prof. Talal Alkhamis||Keywords:||Oil Wells||Issue Date:||2018||Publisher:||Kuwait university - college of graduate studies||Abstract:||This thesis considers the scheduling of work-over rigs for the maintenance of onshore oil wells during a predefined time horizon. The schedule is the result of negotiations between the production and the maintenance department of an oil company. Both departments must agree on a maintenance strategy that does not hinder the production targets while accounting for the company's constraints. Their negotiation gives rise to two problems. The first, which occurs during the planning phase, is a decision problem. This problem illustrates number of rigs required to meet the production target. Briefly, the problem is transformed into an optimization problem that minimizes the deviation of the production from a preset level by a given predetermined date. This problem is modeled as a binary integer program. It is then solved exactly using a branch and bound method, with bounds issued from dual surrogate relaxation. This approach allows to generate a maintenance schedule for the feasible solution. The second, which arises during the execution of the maintenance plan, minimizes the production loss. By rescheduling the maintenance plan resulted from the decision problem considering weighted shortest processing time (WSPT) order to sequence wells maintenance activates on work-over rigs. The thesis formulates it as an integer program, and solves it approximately using a local search heuristic that uses dominance criteria and swaps subsets of maintenance jobs between work-over rigs if this swap satisfies some specific criteria. The thesis provides computational evidence of the suitability of the approach to tackle both large-sized and real life instances from a local oil company. Finally, it offers a sensitivity analysis tool that estimates the merit of hiring additional rigs, and assesses the tradeoff between production loss and resource availability.||URI:||http://hdl.handle.net/123456789/714|
|Appears in Programs:||0480 Statistics & Operations Research|
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