Optimizing Workover Rig Fleet Sizing and Scheduling Using Deterministic and Stochastic Programming Models

We present deterministic and stochastic
programming models for
the workover rig problem, one of the most challenging problems in
the oil industry. In the deterministic approach, an integer linear
programming model is used to determine the rig fleet size and schedule
needed to service wells while maximizing oil production and minimizing
rig usage cost. The stochastic approach is an extension of the deterministic
method and relies on a two-stage stochastic programming model to define
the optimal rig fleet size considering uncertainty in the intervention
time. In this approach, different scenario-generation methods are
compared. Several experiments were performed using instances based
on real-world problems. The results suggest that the proposed methodology
can be used to solve large instances and produces quality solutions
in computationally reasonable times.