Schedule Optimization To Accelerate Offshore Oil Projects While Maximizing Net Present Value in the Presence of Simultaneous Operations, Weather Delays, and Resource Limitations

Abstract

Cost and schedule overruns are endemic problems for offshore oil projects. This can be partly attributed to weather delays, resource limitations, and scheduling risks. The problem is further compounded because of the large number of interdependent activities, such as drilling and platform installation, typically involved in the buildup period of oilfield development. As a result, there is a pressing need to find robust project planning and scheduling models that consider these interacting components and associated risks in offshore oil projects. This study considers three techniques to optimize offshore oil project schedules while accounting for the impact of numerous field activities and potential delay factors; these are mixed-integer linear programming (MILP), single-objective genetic algorithms (SOGAs), and nondominated sorting genetic algorithms (NSGA-II). The study compares the performance of each using a model that integrates field planning with scheduling while accounting for weather delays, resource limitations, and simultaneous operations (SIMOPS; i.e., the ability to conduct more than one activity at once). The first two techniques (MILP and SOGA) optimize the oilfield schedule based on a single objective, which is to maximize net present value (NPV) or minimize project time. However, the maximum NPV schedule may result in a longer project time, whereas the shortest project time may result in a lower NPV. Therefore, the third method using NSGA-II finds Pareto-optimal schedules that balance these competing objectives. Four case studies are provided to compare the MILP and SOGA approaches with the suggested multiobjective NSGA-II.