Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm

The optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR) is addressed. A two-stage stochastic mixed-integer linear fractional programming (SMILFP) model is developed to optimize the levelized cost of energy generated from shale gas. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. To reduce the model size and number of scenarios, we apply a sample average approximation method to generate scenarios based on the real-world EUR data. In addition, a novel solution algorithm integrating the parametric approach and the L-shaped method is proposed for solving the resulting SMILFP problem within a reasonable computational time. The proposed model and algorithm are illustrated through a case study based on the Marcellus shale play, and a deterministic model is considered for comparison.