This article presents a mathematical programing formulation for the optimal management of flowback water in shale gas wells. The formulation accounts for the time‐based generation of the flowback water, the options for treatment, storage, reuse, and disposal. The economic and environmental objectives are considered. The economic objective function is aimed at determining the minimum cost for the fresh water, treatment, storage, disposals, and transportation. The environmental objectives account for the fresh water usage and wastewater discharge. To carry out the water integration, a reuse network including treatment is proposed. Additionally, the model considers seasonal fluctuations in the fresh water availability. A given scheduling for the completion phases of the wells is required to implement the methodology. Finally, an example problem is presented to show the applicability of the proposed methodology. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1634–1645, 2016

This paper presents a mathematical programing formulation for synthesizing water networks associated with shale gas hydraulic fracturing operations while accounting for the system uncertainty. The proposed formulation yields strategic planning that minimizes the cost considering water requirements as well as equipment capacities for treatment technologies, storage units, and disposals. The key uncertainties pertain to the water usage for fracturing and the time-based return of flowback water. The objective function is aimed at the minimization of the total annual cost, which accounts for the operating and capital costs associated with the water network. The developed model addresses the scheduling problem associated with shale gas production, which provides as output the completion phases for all the projected wells. This information is used in estimating the periods with water requirements and where flowback water can be collected. The proposed methodology includes an analysis of the optimal equipment size. An illustrative example is presented to show the capabilities of the proposed methodology.

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