Mixed-integer programming formulations for the optimisation of multi-directional petroleum exploration

Abstract

Directional drilling can be seen as a well-established technology that has advanced in the past few years, allowing higher productivity in petroleum exploration. On the other hand, the design of optimal multi-directional drilling paths has not gained much attention in the literature. Existing models and algorithms for the optimisation of directional drilling paths often disregard important petrophysical attributes or apply heuristic methods, which cannot guarantee the optimality of the generated solutions. In this paper, we employ mixed-integer programming (MIP) to optimize multiple directional drilling paths. This is achieved by integrating a screening step, capable of identifying the most promising target regions in terms of flow capacity, with an MIP model responsible for selecting and sequencing the identified targets. Our approach considers several constraints such as drift angles, maximum wellbore length, and minimum safety distances. In addition, a branch-and-cut algorithm is proposed for solving real-world multi-directional instances of challenging sizes. We carry out a case study in the Campos Basin to validate the proposed models. Preliminary results show that the optimized drilling paths have superior performance when compared to the historical average recovery factor of the Campos Basin.