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. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2017–2036, 2018

中文翻译：

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在混合不确定性下将页岩气货币化为聚合物：随机建模和似然分析

提出了一种基于随机建模方法和似然分析的新颖框架，以解决在原料成分，确定的最终采收率和经济参数不确定的情况下将页岩气转化为聚合物的大规模货币化过程。开发了一种新的随机数据处理策略，以通过生成适当数量的方案来量化原料的可变性。该策略包括基于Kriging的替代模型，样本平均近似值和集成的下降刺激分析曲线。然后，通过在分布式集中式转换网络系统上执行详细的技术经济建模方法来传播原料的可变性。不确定的经济参数被纳入随机模型以估计绩效目标的最大可能性。在不同工厂位置和途径设计的四个案例研究中说明了所建议的策略和模型。结果突出了混合路径的好处，因为它更适合降低项目的经济风险。©2018美国化学工程师学会AIChE J，64：2017–2036，2018