The application of compositional modelling to the integration of realistic re-stimulation strategies and unconventional shale gas supply chain optimization

Shale gas supply chain network optimization received increased focus following the global economic recession that occurred between 2014 and 2016. Increasing the efficiency of upstream and downstream operations while maximizing shale gas project profitability became the best strategy for offsetting low oil and gas prices. This work suggests a multidisciplinary approach for the design and optimization of a given shale gas supply chain network. This integrated approach incorporates realistic drilling, completion and stimulation strategies through the compositional modelling of a geologically heterogeneous shale gas reservoir. Previous work in this area considered mixed integer linear programming (MILP) optimization models with minimal consideration for pragmatic shale gas reservoir development. This resulted in grossly over-estimated shale gas project NPV's and impractical payback times. A 3D coupled compositional reservoir model is constructed using CMG's software suites, to simulate drilling, completion, stimulation (DCS) and well pad production processes for a supply chain network. Utilizing geological parameters, characteristic of the Marcellus shale, this model generates realistic wastewater and gas rate decline profiles as well as the temporal variation of shale gas components, which are inputs for Quasi-MINLP optimization in General Algebraic Modelling System (GAMS). The results show that the optimization of shale gas production and project NPV are rarely obtained from singular and mathematically convenient well pad design configurations. Reservoir heterogeneity and realistic DCS strategies have a huge influence on the techno-economics of shale gas projects. This paper reveals a conservative and yet credible approach to optimizing well pad designs, waste water recycle structures and supply chain networks, while minimizing shale gas re-stimulation costs.