Optimal Shale Gas Flowback Water Desalination under Correlated Data Uncertainty

Abstract

Optimal flowback water desalination is critical to improve overall efficiency and sustainability of shale gas production. Nonetheless, great uncertainty in well data from shale plays strongly hinders the design task. In this work, we introduce a new stochastic multiscenario optimization model for the robust design of desalination systems under uncertainty. A zero-liquid discharge (ZLD) system composed by multiple-effect evaporation with mechanical vapor recompression (MEE-MVR) is proposed for the desalination of high-salinity shale gas flowback water. Salinity and flowrate of flowback water are both considered as uncertain design parameters, which are described by correlated scenarios with given probability of occurrence. The set of scenarios is generated via Monte Carlo sampling technique from a multivariate normal distribution. ZLD operation is ensured by the design constraint that allows brine concentration near to salt saturation conditions for all scenarios. The stochastic multiscenario nonlinear programming (NLP) model is optimized in GAMS, through the minimization of the expected total annualized cost. Risk analysis based on cumulative probability curves is performed in the uncertain search space, to support decision-makers towards the selection of more robust ZLD desalination systems applied to shale gas flowback water.