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

Abstract

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.