Some of the most important and expensive activities in onshore oil and gas field development involve the use of drilling rigs. This paper presents a method to optimize onshore drilling rig fleet size and schedule considering reservoir management and operational objectives, maximizing production volume, meeting production targets and/or minimizing rig costs.
The problem of determining the sequence of wells to be drilled by each rig is called the Rig Scheduling Problem (RSP). The primary objective of the reservoir management team is to maximize the production volume or meet production targets by creating an operating plan and then creating a rig schedule to minimize rig costs and overall operations costs while meeting production targets.
“Combined Optimization” is an example of a method that is used in the Rig Activity Scheduler. It’s objective is to schedule the rigs to minimize the transportation cost, while meeting or exceeding the target production delay. Higher performing wells are scheduled as early as possible, and an equal number of wells are assigned to each individual rig. Another example of “combined optimization” integrates drilling rig scheduling with reservoir simulation. The algorithm aims to maintain overall field production, while seeking to reduce the net distance travelled by the rigs and the number of rigs uses. No actual algorithm is used, instead the work focuses on reducing the net distance travelled.
The existing RSP approaches still have challenges. There are three primary challenges with the Rig Scheduling Problem. The first is the complexity of the optimization problem, because of the number of variables. Second challenge is that typical approaches do not align reservoir management objectives with operational objectives. The final challenge is that most approaches are deterministic and do not account for variability and statistical variation that occurs throughout operations.
The full paper addresses the Rig Scheduling Problem (RSP) by taking production systems perspectives to incorporate more systematically variable uncertainty and coupling between reservoir management objectives and operation objectives in applying simulation-based optimization.
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