(342j) Multi-Period Design of Shale Gas Supply Chain Network to Handle Temporal Variability in Wastewater Volume from Shale Gas Wells

In the United States, natural gas is one of the most important energy sources and the contribution of shale gas production to total natural gas production has increased significantly, due to the recent advances in the horizontal drilling and hydraulic fracturing technologies which increased the number of economically feasible shale gas resources [1]. This shale gas production system is generally complex and multi-stage, and requires a variety of decisions in each stage regarding freshwater acquisition, shale gas production and processing, wastewater production and management, inventory, and transportation. Thus, the shale gas development is usually characterized by high capital and operating expenditures, and thus, discerning the optimal design and planning of the shale gas supply chain has great potential to impact the U.S. economy [2], [3].

Recently, many studies have been performed on the optimization of the long-term planning and development of shale gas supply chain network (SGSCN) [2]–[6]. However, few of them described the closing of existing wastewater treatment facilities and opening of new wastewater treatment facilities to account for temporal variability in wastewater produced during shale gas production [7]. If we permanently use facilities established at the beginning of the planning horizon, the insufficient capacity in some time periods may result in negative environmental implications, while the excessive capacity in later time periods may lead to detrimental impacts on economic performance. Thus, to find a good balance between these two conflicting objectives, it makes sense to consider the opening and closing of wastewater treatment facilities which should be determined at each time period based on the time-varying wastewater production volume.

Motivated by this consideration, we developed a mixed integer linear programming (MILP) model to integrate the planning of SGSCN and the dynamic allocation of centralized wastewater treatment facilities and onsite wastewater treatment facilities. The objective is to maximize the net present value while considering the benefit from opening and closing of the wastewater treatment facilities. Based on the MILP model, the major decisions are the schedule of hydraulic fracturing operations to satisfy the product demands, and the locations and capacities of the wastewater treatment facilities as well as their timings of opening and closing to handle the generated wastewater. The proposed SGSCN optimization model is implemented on an illustrative example by utilizing real wastewater production data from the Marcellus shale play, and the results demonstrate its advantages over the case using (conventional) permanent wastewater treatment facilities.

[1] E. I. A. (US E. I. Administration), “Annual Energy Outlook 2018 with projections to 2050.” Office of Energy Analysis, US Department of Energy Washington, DC, 2018.

[2] J. Gao 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 Sustain. Chem. Eng., vol. 3, no. 7, pp. 1282–1291, 2015.

[3] D. C. Cafaro and I. E. Grossmann, “Strategic planning, design, and development of the shale gas supply chain network,” AIChE J., vol. 60, no. 6, pp. 2122–2142, 2014.

[4] L. Yang, I. E. Grossmann, M. S. Mauter, and R. M. Dilmore, “Investment optimization model for freshwater acquisition and wastewater handling in shale gas production,” AIChE J., vol. 61, no. 6, pp. 1770–1782, 2015.

[5] D. Oke, T. Majozi, R. Mukherjee, D. Sengupta, and M. M. El-Halwagi, “Simultaneous Energy and Water Optimisation in Shale Exploration,” Processes, vol. 6, no. 7, p. 86, 2018.

[6] F. Y. Al-Aboosi and M. M. El-Halwagi, “A stochastic optimization approach to the design of shale gas/oil wastewater treatment systems with multiple energy sources under uncertainty,” Sustainability, vol. 11, no. 18, p. 4865, 2019.

[7] A. J. Kondash, N. E. Lauer, and A. Vengosh, “The intensification of the water footprint of hydraulic fracturing,” Sci. Adv., vol. 4, no. 8, p. eaar5982, 2018.