Mixed-integer linear programming for scheduling unconventional oil field development

The scheduling of drilling and hydraulic fracturing of wells in an unconventional oil field plays an important role in the profitability of the field. A key challenge arising in this problem is the requirement that neither drilling nor oil production can be done at wells within a specified neighborhood of a well being fractured. We propose a novel mixed-integer linear programming (MILP) formulation for determining a schedule for drilling and fracturing wells in an unconventional oil field. We also derive an alternative formulation which provides stronger relaxations. In order to apply the MILP model for scheduling large fields, we derive a rolling horizon approach that solves a sequence of coarse time-scale MILP instances to obtain a solution at the daily time scale. We benchmark our MILP-based rolling horizon approach against a baseline scheduling algorithm in which wells are developed in the order of their discounted production revenue. Our experiments on synthetically generated instances demonstrate that our MILP-based rolling horizon approach can improve profitability of a field by 4-6%.

Automated summary

The scheduling of drilling and hydraulic fracturing in an unconventional oil field plays a crucial role in profitability. The study proposes a novel mixed-integer linear programming formulation and a rolling horizon approach to improve profitability by 4-6% based on experiments with synthetic instances.