Efficient Drilling Sequence Optimization Using Heuristic Priority Functions

Drilling sequence optimization is a common challenge faced in the oil and gas industry, and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drilling queue is currently designed manually based on engineering heuristics. In this paper, we combined the heuristic priority functions (HPFs) with traditional optimizers to boost the optimization efficiency at a lower computational cost to speed up the decision-making process. The HPFs are constructed to map the individual well properties such as well index and interwell distance to the well priority values. As the name indicates, wells with higher priority values will be drilled earlier in the queue. The HPFs are a comprehensive metric of interwell communication and displacement efficiency. For example, injectors with fast support to producers, or producers with a better chance to drain the unswept region, tend to have high scores. They contain components that weigh the different properties of a well. These components are then optimized during the optimization process to generate the beneficial drilling sequences. Embedded with reservoir engineering heuristics, the priority function (PF) helps the optimizer focus on exploring scenarios with promising outcomes. The proposed HPFs, combined with the genetic algorithm (GA), have been tested through drilling sequence optimization problems for the Brugge Field and Olympus Field. Optimizations that are directly performed on the drilling sequence are used as reference cases. Different continuous/categorical parameterization schemes and various forms of HPFs are also investigated. Our exploration reveals that the HPF including well type, constraints, well index, distance to existing wells, and adjacent oil in place (OIP) yields the best outcome. The proposed approach achieved a better optimization starting point (similar to 5 to 18% improvement due to more reasonable drilling sequence rather than random guess), a faster convergence rate (results stabilized at 12 vs. 30 iterations), and a lower computational cost [150 to 250 vs. 1,300 runs to achieve the same net present value (NPV)] over the reference methods. Similar performance improvement was also observed in another application to a North Sea-type reservoir. This demonstrated the general applicability of the proposed method. The use of HPFs improves the efficiency and reliability of drilling sequence optimization compared with the traditional methods that directly optimize the sequence. They can be easily embedded in either commercial or research simulators as an independent module. In addition, they are also an automatic process that fits well with iterative optimization algorithms.