Development of Shale Gas Supply Chain Network under Market Uncertainties

Chebeir, Jorge; Geraili, A.; Romagnoli, José A.

doi:10.3390/en10020246

Cited by 16 publications

(5 citation statements)

References 33 publications

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“…Robertson et al [45] have used the NLP model to solve refinery production scheduling and unit operation optimization problems. A two-stage stochastic programming model is proposed to describe and optimize the shale gas supply chain network under uncertain conditions [46]. Although applicable to the treatment of problems involving blending, continuous flow processing, production and distribution, strategic/tactical planning, etc., mathematical programming models are not flexible in dealing with the stochasticity, uncertainty and complexity of structure and interaction typically encountered in supply chains [47,48].…”

Section: State-of-the-art Literature Review On Energy Supply Chain Modelsmentioning

confidence: 99%

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

2021

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The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent’s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent’s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent’s uncertain interactions and the dynamically changing structure.

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Section: State-of-the-art Literature Review On Energy Supply Chain Modelsmentioning

confidence: 99%

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

2021

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“…Many recent publications, such as Guo et al [34], Gao et al [35], Chebeir et al [36], Chen et al [37], Drouven and Grossmann [38], He et al [39], and Wang et al [40] have discussed different optimal modeling approaches for shale gas water management systems with socio-economic and environmental concerns. All of the above studies are confined to only uncertain modeling approaches, and have not discussed uncertainty among parameters' values; however, Zhang et al [22] incorporated vagueness among parameters and represented it by fuzzy and stochastic quantification methodology.…”

Section: Research Gaps and Contributionmentioning

confidence: 99%

Neutrosophic Optimization Model and Computational Algorithm for Optimal Shale Gas Water Management under Uncertainty

2019

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Shale gas energy is the most prominent and dominating source of power across the globe. The processes for the extraction of shale gas from shale rocks are very complex. In this study, a multiobjective optimization framework is presented for an overall water management system that includes the allocation of freshwater for hydraulic fracturing and optimal management of the resulting wastewater with different techniques. The generated wastewater from the shale fracking process contains highly toxic chemicals. The optimal control of a massive amount of contaminated water is quite a challenging task. Therefore, an on-site treatment plant, underground disposal facility, and treatment plant with expansion capacity were designed to overcome environmental issues. A multiobjective trade-off between socio-economic and environmental concerns was established under a set of conflicting constraints. A solution method—the neutrosophic goal programming approach—is suggested, inspired by independent, neutral/indeterminacy thoughts of the decision-maker(s). A theoretical computational study is presented to show the validity and applicability of the proposed multiobjective shale gas water management optimization model and solution procedure. The obtained results and conclusions, along with the significant contributions, are discussed in the context of shale gas supply chain planning policies over different time horizons.

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“…To illustrate, if two uncertain parameters described by three nodes (high, medium, and low) and four multiple periods represented by four seasons (e.g., spring, summer, fall, and winter) were considered, Hence, at the end of the fourth period, 3 8 scenarios were generated. Similar approaches have been adopted in the literature of other applications [29,41,49]. The next step was to keep fewer scenarios possible to ensure that the problem of stochastic optimization could be solved with a reasonable computational effort.…”

Section: Generating Scenario Tree For Uncertain Parametersmentioning

confidence: 99%

“…Chebeir et al [29] developed a model to describe and optimize the shale gas supply chain network by using a two-stage stochastic programming model. The uncertainty in prices of natural gas and natural gas liquids (NGL) products is handled through using a scenario-based method.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

Al-Aboosi

El‐Halwagi

2019

Sustainability

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The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.

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Development of Shale Gas Supply Chain Network under Market Uncertainties

Cited by 16 publications

References 33 publications

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

Neutrosophic Optimization Model and Computational Algorithm for Optimal Shale Gas Water Management under Uncertainty

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

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