TY - GEN
T1 - Stochastic programming approach to optimal design and operations of shale gas supply chain under uncertainty
AU - Gao, Jiyao
AU - You, Fengqi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - In this paper, we propose the first stochastic model addressing the optimal design and operations of the comprehensive shale gas supply chain, where uncertainties of estimated ultimate recovery (EUR) in each shale well are considered. The resulting mixed-integer linear programming (MILP) model covers the well-to-wire life cycle of shale gas, which consists of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. In order to reduce the model size and the number of scenarios, we use a sample average approximation approach to generate scenarios based on the EUR distribution derived from actual historical data. To demonstrate the proposed stochastic model and solution approach, we present a case study based on Marcellus shale play to maximize the total expected profit of this shale gas supply chain network.
AB - In this paper, we propose the first stochastic model addressing the optimal design and operations of the comprehensive shale gas supply chain, where uncertainties of estimated ultimate recovery (EUR) in each shale well are considered. The resulting mixed-integer linear programming (MILP) model covers the well-to-wire life cycle of shale gas, which consists of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. In order to reduce the model size and the number of scenarios, we use a sample average approximation approach to generate scenarios based on the EUR distribution derived from actual historical data. To demonstrate the proposed stochastic model and solution approach, we present a case study based on Marcellus shale play to maximize the total expected profit of this shale gas supply chain network.
KW - Natural gas
KW - Stochastic processes
KW - Supply chains
KW - Transportation
KW - Uncertainty
KW - Wastewater
UR - http://www.scopus.com/inward/record.url?scp=84962019703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962019703&partnerID=8YFLogxK
U2 - 10.1109/CDC.2015.7403267
DO - 10.1109/CDC.2015.7403267
M3 - Conference contribution
AN - SCOPUS:84962019703
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6656
EP - 6661
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -