Guerra, OJ;
Calderon, AJ;
Papageorgiou, LG;
Reklaitis, GV;
(2019)
Integrated shale gas supply chain design and water management under uncertainty.
AIChE Journal
, 65
(3)
pp. 924-936.
10.1002/aic.16476.
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Abstract
The development of shale gas resources is subject to technical challenges and markedly affected by volatile markets that can undermine the development of new projects. Consequently, stakeholders can greatly benefit from decision‐making support tools that integrate the complexity of the system along with the uncertainties inherent to the problem. Accordingly, a general methodology is proposed in this work for the evaluation of integrated shale gas and water supply chains under uncertainty. First, key parametric uncertainties are identified from a candidate pool via a global sensitivity analysis based on a deterministic optimization model. Then, a two‐stage stochastic model is developed considering only the key uncertain parameters in the problem. Moreover, the merits of modeling uncertainty and implementing the stochastic solution approach are evaluated using the expected value of perfect information and the value of the stochastic solution metrics. Furthermore, the conditional value‐at‐risk approach was implemented to evaluate different risk‐aversion levels and the corresponding impacts on the shale gas development plan. The proposed methodology is illustrated through two real‐world case studies involving six and eight potential well‐pad locations and two options of well‐pad layouts.
Type: | Article |
---|---|
Title: | Integrated shale gas supply chain design and water management under uncertainty |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1002/aic.16476 |
Publisher version: | https://doi.org/10.1002/aic.16476 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | shale gas, water management, supply chain design, uncertainty, stochastic optimization framework |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
URI: | https://discovery.ucl.ac.uk/id/eprint/10070519 |
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