(181h) A Leader-Follower Game-Based Life Cycle Optimization Framework and Shale Gas Supply Chain Application

In the past decade, accompanying the rapid development of shale gas industry, concerns are raised regarding the environmental impacts of shale gas production, among which the greenhouse gas (GHG) emissions is of special interest to both academia and industry. There have been a huge body of literature focusing on evaluating the life cycle GHG emissions of shale gas, most of which are based on life cycle assessment methodology [1, 2]. Meanwhile, some publications focus on optimization of shale gas supply chains; issues such as supply chain planning [3], process design [4], water management [5], uncertainties [6], and GHG emissions [7] were addressed. However, all of these literature consider a centralized model, where a single decision-maker is assumed with absolute power in the supply chain design and management. Nevertheless, in practice the management of a shale gas supply chain is normally decentralized and run by different companies [8]. As a consequence, the optimal strategy obtained from a centralized model can be practically infeasible [9]. Therefore, there is an urgent need to properly address the non-cooperative relationship between multiple stakeholders in the life cycle optimization of a shale gas supply chain [10].

To address this challenge, we propose a novel leader-follower game-based LCO modeling framework, which integrates both the leader-follower Stackelberg game and LCO approach [9, 11, 12]. This holistic modeling framework enables us to simultaneously address the trade-offs between conflicting objectives as well as the interactions between different players. As the initiator in the shale gas supply chain, the upstream shale gas producer is identified as the leader. Due to the key role of leader in a game, the producer not only enjoys the priority to make decisions first, but senses the responsibility to mitigate the life cycle GHG emission embedded in the final product in addition to pursuing profit. The midstream player shale gas processor is identified as the follower, who will take actions rationally according to the leaderâ??s decisions to pursue its own profit. The resulting problem is a multiobjective mixed-integer bilevel linear programming (MIBLP) problem that cannot be solved directly by any off-the-shelf solver. To address this computational challenge, we preent a novel projection-based reformulation and decomposition algorithm for efficient solution of this problem.

To illustrate the application of proposed modeling framework and solution algorithm, we present a case study based on Marcellus shale play, where a â??cradle-to-gateâ? system boundary is chosen covering the whole shale gas production system from upstream shale sites to midstream processing plants. Multiple strategic decisions are taken into account, including the well drilling schedule, water management, installation of gathering pipelines, allocation, capacity and design of processing plants, and selection of processing contract. In order to demonstrate the advantage of the game-based model, we further present a detailed comparison with classical centralized models. Based on a case study of Marcellus shale play, the leaderâ??s net present value (NPV) ranges from $43.0 M to $154.7 M, and the corresponding total greenhouse gas (GHG) emissions range from 1.03 Mt CO₂-eq to 2.37 Mt CO₂-eq. Moreover, the centralized model gives over-optimistic solutions. The highest total NPV obtained from centralized model is 10.2% higher than that in the game-based model, and the lowest GHG emission is 3.4% lower.

References

[1] C. L. Weber and C. Clavin, "Life Cycle Carbon Footprint of Shale Gas: Review of Evidence and Implications," Environmental Science & Technology, vol. 46, pp. 5688-5695, 2012.

[2] G. A. Heath, P. Oâ??Donoughue, D. J. Arent, and M. Bazilian, "Harmonization of initial estimates of shale gas life cycle greenhouse gas emissions for electric power generation," Proceedings of the National Academy of Sciences, vol. 111, pp. E3167-E3176, 2014.

[3] D. C. Cafaro and I. E. Grossmann, "Strategic planning, design, and development of the shale gas supply chain network," AIChE Journal, vol. 60, p. 21, 2014.

[4] C. He and F. You, "Toward more cost-effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation-based optimization," AIChE Journal, vol. 61, pp. 1209-1232, 2015.

[5] 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 Journal, vol. 61, pp. 1770-1782, 2015.

[6] J. Gao and F. You, "Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm," AIChE Journal, vol. 61, pp. 3739-3755, 2015.

[7] 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 Sustainable Chemistry & Engineering, vol. 3, pp. 1282-1291, 2015.

[8] G. Cachon and S. Netessine, "Game Theory in Supply Chain Analysis," in Handbook of Quantitative Supply Chain Analysis. vol. 74, D. Simchi-Levi, S. D. Wu, and Z.-J. Shen, Eds., ed: Springer US, 2004, pp. 13-65.

[9] H. Von Stackelberg, Market structure and equilibrium: Springer Science & Business Media, 2010.

[10] D. J. Garcia and F. You, "Supply chain design and optimization: Challenges and opportunities," Computers & Chemical Engineering, vol. 81, pp. 153-170, 2015.

[11] D. Yue and F. You, "Game-theoretic modeling and optimization of multi-echelon supply chain design and operation under Stackelberg game and market equilibrium," Computers & Chemical Engineering, vol. 71, pp. 347-361, 2014.

[12] D. J. Yue, M. A. Kim, and F. Q. You, "Design of Sustainable Product Systems and Supply Chains with Life Cycle Optimization Based on Functional Unit: General Modeling Framework, Mixed-Integer Nonlinear Programming Algorithms and Case Study on Hydrocarbon Biofuels," Acs Sustainable Chemistry & Engineering, vol. 1, pp. 1003-1014, 2013.