A three-level optimization methodology for the partitioning of shale gas wellpad groups

Shale gas exploitation incurs considerable financial risk due to the high uncertainty of gas production; reducing the full life-cycle costs would play an important role in achieving expected economic benefits. In this paper, a three-level optimization methodology is proposed to optimally partition shale gas wellpad groups with the objective of minimizing the construction costs of the gas-gathering system. The first-level model is built to determine the annual optimal infield battery sites and the subordinated relationship among wellpads and infield batteries, aiming to minimize the construction costs of the gas-gathering system for each development year. The second-level model is proposed to determine the global optimal infield battery sites for the entire field life by using the hierarchical clustering method to divide all annual optimal infield battery sites into several limited clusters. The third-level model is established to determine a globally optimal subordinated relationship among wellpads and infield battery sites, with the objective of minimizing the total distances from the wellpads to the global optimal infield battery sites. The unconventional features, i.e., continuous tie-in, production decline and shut-down or abandonment of shale gas wellpads during field development, have been considered and integrated into the three-level methodology. The hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is used to solve the first- and third-level models, whereas the single-linkage clustering algorithm is adopted to solve the second-level model. Finally, a practical application of the methodology is performed to validate its effectiveness in partitioning wellpad groups in a real shale gas field in Sichuan Province, China. The achievements provide an effective method for partitioning shale gas wellpads in either the conceptual design or the following realistic development phases.