Shale gas has emerged as a potential resource to transform the global energy market. Nevertheless, gas extraction from tight shale formations is only possible after horizontal drilling and hydraulic fracturing, which generally demand large amounts of water. Part of the ejected fracturing fluid returns to the surface as flowback water, containing a variety of pollutants. For this reason, water reuse and water recycling technologies have received further interest for enhancing overall shale gas process efficiency and sustainability. Water pretreatment systems (WPSs) can play an important role for achieving this goal. This paper introduces a new optimization model for WPS simultaneous synthesis, especially developed for flowback water from shale gas production. A multistage superstructure is proposed for the optimal WPS design, including several water pretreatment alternatives. The mathematical model is formulated via generalized disjunctive programming (GDP) and solved by re-formulation as a mixed-integer nonlinear programming (MINLP) problem, to minimize the total annualized cost. Hence, the superstructure allows identifying the optimal pretreatment sequence with minimum cost, according to inlet water composition and wastewater-desired destination (i.e., water reuse as fracking fluid or recycling). Three case studies are performed to illustrate the applicability of the proposed approach under specific composition constraints. Thus, four distinct flowback water compositions are evaluated for the different target conditions. The results highlight the ability of the developed model for the cost-effective WPS synthesis, by reaching the required water compositions for each specified destination.
To address water planning decisions in shale gas operations, we present a novel water management optimization model that explicitly takes into account the effect of high concentration of total dissolved solids (TDS), and its temporal variation in the impaired water. The model comprises different water management strategies: a) direct wastewater reuse, which is possible due to the new additives tolerant to high TDS concentration but at the expense of increasing the costs; b) wastewater treatment, taking separately into account pre-treatments, softening and desalination technologies and c) send to Class II disposal sites.The objective is to maximize the "sustainability profit" determining flowback destination (reuse, degree of treatment or disposal), the fracturing schedule, fracturing fluid composition and the number of water storage tanks needed at each period of time.Due to the rigorous determination of TDS in all water streams, the model is a non-convex MINLP model that is tackled in two steps: first, an MILP model is solved based on McCormick relaxations; next, the binary variables that determine the fracturing schedule are fixed, and a smaller MINLP is solved.
Thermal membrane distillation (MD) is an emerging technology to desalinate highsalinity wastewaters, including shale gas produced water to reduce the corresponding water footprint of fracturing operations. In this work, we introduce a rigorous optimization model with energy recovery for the synthesis of multistage direct contact membrane distillation (DCMD) system. The mathematical model (implemented in GAMS software) is formulated via generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP). To maximize the total amount of water recovered, the outflow brine is fixed close to salt saturation conditions (300 g·kg -1 water) approaching zero liquid discharge (ZLD).A sensitivity analysis is performed to evaluate the system's behavior under different uncertainty sources such as the heat source availability and inlet salinity conditions. The results emphasize the applicability of this promising technology, especially with low steam cost or waste heat, and reveal variable costs and system configurations depending on inlet conditions. For a produced water salinity ranging from 150 g·kg -1 water to 250 g·kg -1 water based on Marcellus play, an optimal treating cost are between 11.5 and 4.4 US$ m -3 is obtained when using low-cost steam. This cost can decrease to 2.8 US$ m -3 when waste heat from shale gas operations is used.
This paper introduces a comprehensive study of the Life Cycle Impact Assessment (LCIA) of water management in shale gas exploitation. First, we present a comprehensive study of wastewater treatment in the shale gas extraction, including the most common technologies for the pretreatment and three different desalination technologies of recent interest: Single and Multiple-Effect Evaporation with Mechanical Vapor Recompression and Membrane Distillation. The analysis has been carried out through a generic Life Cycle Assessment (LCA) and the ReCiPe metric (at midpoint and endpoint levels), considering a wide range of environmental impacts. The results show that among these technologies Multiple-Effect Evaporation with Mechanical Vapor Recompression (MEE-MVR) is the most suitable technology for the wastewater treatment in shale gas extraction, taking into account its reduced environmental impact, the high water recovery compared to other alternatives as well as the lower cost of this technology. We also use a comprehensive water management model that includes previous results that takes the form of a new Mixed-Integer Linear Programming (MILP) bi-criterion optimization model to address the profit maximization and the minimization Life Cycle Impact Assessment (LCIA), based on its results we discuss the main tradeoffs between optimal operation from the economic and environmental points of view.
In this work, a mixed-integer linear programming (MILP) model is developed to address optimal shale gas-water management strategies among shale gas companies that operate relatively close. The objective is to compute a distribution of water-related costs and profit among shale companies to achieve a stable agreement on cooperation among them that allows increasing total benefits and reducing total costs and environmental impacts. We apply different solution methods based on cooperative game theory: The Core, the Dual Core, the Shapley value, and the minmax Core. We solved different case studies including a large problem involving four companies and 207 wells. In this example, individual cost distribution (storage cost, freshwater withdrawal cost, transportation cost, and treatment cost) assigned to each player is included. The results show that companies that adopt cooperation strategies improve their profits and enhance the sustainability of their operations through the increase in recycled water.
Sustainable and efficient desalination is required to treat the large amounts of highsalinity flowback water from shale gas extraction. Nevertheless, uncertainty associated with well data (including water flowrates and salinities) strongly hampers the process design task. In this work, we introduce a new optimization model for the synthesis of zero-liquid discharge (ZLD) desalination systems under uncertainty. The desalination system is based on multiple-effect evaporation with mechanical vapor recompression (MEE-MVR). Our main objective is energy efficiency intensification through brine discharge reduction, while accounting for distinct water feeding scenarios. For this purpose, we consider the outflow brine salinity near to salt saturation condition as a design constraint to achieve ZLD operation. In this innovative approach, uncertain parameters are mathematically modelled as a set of correlated scenarios with known probability of occurrence. The scenarios set is described by a multivariate normal distribution generated via a sampling technique with symmetric correlation matrix. The stochastic multiscenario non-linear programming (NLP) model is implemented in GAMS, and optimized by the minimization of the expected total annualized cost. An illustrative case study is carried out to evaluate the capabilities of the proposed new approach. Cumulative probability curves are constructed to assess the financial risk related to uncertain space for different standard deviations of expected mean values. Sensitivity analysis is performed to appraise optimal system performance for distinct brine salinity conditions. This methodology represents a useful tool to support decision-makers towards the selection of more robust and reliable ZLD desalination systems for the treatment of shale gas flowback water.Keywords: Shale gas flowback water; Multiple-effect evaporation with mechanical vapor recompression (MEE-MVR); Zero-liquid discharge (ZLD); Uncertainty; Risk management; Robust design.3
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