Monetizing shale gas to polymers under mixed uncertainty: Stochastic modeling and likelihood analysis

Chang He, Ming Pan, Bingjian Zhang, Qinglin Chen, Fengqi You, Jingzheng Ren

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


A novel framework based on stochastic modeling methods and likelihood analysis to address large-scale monetization processes of converting shale gas to polymers under the mixed uncertainties of feedstock compositions, estimated ultimate recovery, and economic parameters is presented. A new stochastic data processing strategy is developed to quantify the feedstock variability through generating the appropriate number of scenarios. This strategy includes the Kriging-based surrogate model, sample average approximation, and the integrated decline-stimulate analysis curve. The feedstock variability is then propagated through performing a detailed techno-economic modeling method on distributed-centralized conversion network systems. Uncertain economic parameters are incorporated into the stochastic model to estimate the maximum likelihood of performance objectives. The proposed strategy and models are illustrated in four case studies with different plant locations and pathway designs. The results highlight the benefits of the hybrid pathway as it is more amenable to reducing the economic risk of the projects.

Original languageEnglish
Pages (from-to)2017-2036
Number of pages20
JournalAICHE Journal
Issue number6
Publication statusPublished - 1 Jun 2018


  • likelihood analysis
  • mixed uncertainty
  • shale gas
  • stochastic modeling
  • techno-economic modeling

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

Cite this