Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains

Water management has increasingly become a hotspot in shale gas supply chains. This study develops a comprehensive modeling framework for the mixed noise-driven shale gas-water supply chains, which is integrated with techniques of system dynamics model and two-stage stochastic risk-aversion programming. White and colored noises are used for addressing stochastic characterization of shale gas productivity. Regional water resources carrying capacity is reflected based on the system dynamics model. The developed model cannot only effectively address stochastic parameters in the objective and constraints, but also offer a linkage between the pre-regulated policies and corresponding economic implications raised from improper policies. A Marcellus-based case in Pennsylvania is then performed to validate the applicability of the developed model. Results reveal that shale gas production curve is more sensitive to colored and mixed noises than white noise. High flowback and recycle rates would increase carrying capacity of shale wells by 1.8% in 2023. An increase in weighting factor (e.g., from 0 to 30) would induce a rise in system cost (e.g., from $1.22x10(11) to $1.34x10(11)) but a reduction in conditional value-at risk value (e.g., from $6.83x10(10) to $3.83x10(10)). Weighting factor thus can be served as an indicator to show how much the decision makers' attention to system risk. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study develops a comprehensive modeling framework for mixed noise-driven shale gas-water supply chains using system dynamics model and stochastic risk-aversion programming. The results show that high flowback and recycle rates can increase shale wells' carrying capacity, and an increase in weighting factor would increase system cost but reduce conditional value-at risk value.