(442c) A Novel Framework for Shale Gas Supply Chain Network Considering MPC-Based Pumping Schedule of Hydraulic Fracturing in Unconventional Reservoirs
Several studies have been conducted to illustrate shale gas supply chain network (SGSCN) in an economically viable manner [2-7], but they did not consider the effect of final fracture geometry with respect to shale gas production and wastewater recovery. The amounts of shale gas and wastewater produced from shale wells are determined by the final fracture geometry at the end of pumping [8, 9], which is highly dependent upon the amount and properties of injected fresh water. For example, a 10% deviation from the desired fracture length in hydraulic fracturing can cause a 50% decrease in the amounts of produced shale gas [8, 10]. Therefore, the final fracture geometry, and the amounts and properties of injected freshwater should be considered in determining an optimal SGSCN configuration.
Motivated by these considerations, a new framework is developed to integrate a model predictive control (MPC)-based pumping schedule of hydraulic fracturing and SGSCN model; this integrated approach enables understanding the complex connections between hydraulic fracturing, shale gas management, and wastewater management. SGSCN consists of two main parts: (1) water network for ensuring the freshwater supply to shale wells and wastewater treatment during shale gas production; (2) shale gas network for separating shale gas, transporting and storing natural gas, and generating electricity. Based on this developed framework, the optimal SGSCN configuration will be determined by maximizing the overall profit over a multi-site and multi-period planning horizon by formulating a mixed integer linear programming problem. The proposed model has been applied to two case studies based on Marcellus shale play to demonstrate its superiority over other existing approaches.
 Wang, Q., et al., Natural gas from shale formationâthe evolution, evidences and challenges of shale gas revolution in United States. Renewable and Sustainable Energy Reviews, 2014. 30: p. 1-28.
 Gao, J. and F. You, Shale gas supply chain design and operations toward better economic and life cycle environmental performance: MINLP model and global optimization algorithm. ACS Sustainable Chemistry & Engineering, 2015. 3(7): p. 1282-1291.
 Chebeir, J., A. Geraili, and J. Romagnoli, Development of Shale Gas Supply Chain Network under Market Uncertainties. Energies, 2017. 10(2): p. 246.
 Gao, Jiyao, and Fengqi You. "Optimal design and operations of supply chain networks for water management in shale gas production: MILFP model and algorithms for the waterâenergy nexus." AIChE Journal 61.4 (2015): 1184-1208.
 Cafaro, D.C. and I.E. Grossmann, Strategic planning, design, and development of the shale gas supply chain network. AIChE Journal, 2014. 60(6): p. 2122-2142.
 Lira-BarragaÌn, Luis Fernando, et al. "Optimal water management under uncertainty for shale gas production." Industrial & Engineering Chemistry Research 55.5 (2016): 1322-1335.
 Arredondo-RamÃrez, Karla, JosÃ© MarÃa Ponce-Ortega, and Mahmoud M. El-Halwagi. "Optimal planning and infrastructure development for shale gas production." Energy Conversion and Management 119 (2016): 91-100.
 Siddhamshetty, P., et al., Feedback control of proppant bank heights during hydraulic fracturing for enhanced productivity in shale formations. AIChE Journal, 2018. 64(5): p. 1638-1650.
 Yang, Seeyub, Prashanth Siddhamshetty, and Joseph Sang-Il Kwon. "Optimal pumping schedule design to achieve a uniform proppant concentration level in hydraulic fracturing." Computers & Chemical Engineering 101 (2017): 138-147.
 Cipolla, C., E. Lolon, and M. Mayerhofer, The Effect of Proppant Distribution and Un-propped Fracture Con-ductivity on Well Performance in Unconventional Gas Reservoirs. SPE, 2009. 119368.