Optimal design and operation for simultaneous shale gas NGL recovery and LNG re-gasification under uncertainties

Wang, Meiqian; Xu, Qiang

doi:10.1016/j.ces.2014.03.021

Cited by 30 publications

(14 citation statements)

References 10 publications

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“…In addition, researchers have designed various novel hybrid pathways to improve the energy‐economic‐environmental performances of shale gas projects. There are two subgroups of hybrid pathway designs, namely external integration, where the raw shale gas can co‐feed with other types of resource, that is, agricultural/forest residues, bioethanol, switchgrass, PG, and so forth and internal integration, where the methane and NGLs included in raw shale gas have the potential to be integrated within the processing and production systems …”

Section: Literature Reviewmentioning

confidence: 99%

“…The uncertain set of compositions was derived from various shale plays and publications, instead of the field data from the same shale play. Wang and Xu addressed the optimal design and synthesis of integrated NGLs recovery and LNG regasification under uncertain feed rates. They assumed a standard normal distribution for uncertain feed rates.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Pan

Zhang

et al. 2018

AIChE Journal

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

Pan

Zhang

et al. 2018

AIChE Journal

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“…The extraction and utilization of this cold energy has great potential to improve the combined efficiency and to reduce the overall capital and operating costs. Various methods for the LNG cold energy recovery have been proposed, evaluated, and optimized in the literature, including the integration with power generation [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70], air separation or other low-temperature fractionation [71][72][73][74][75][76], seawater desalination [77,78], agro food deep-freezing and space air conditioning [79][80][81], etc. Utilizing the LNG cold energy for power generation is less influ-enced by the ambient condition, downstream market supply and demand, transportation etc., and attracts the most attention worldwide.…”

Section: Cold Energy Recovery Considerationmentioning

confidence: 99%

“…efficiency may be associated with higher investment costs and less robustness of the system to accommodate process fluctuations, and these features will bring more complexity in the process design, optimization, and operation [53,64,73,76]. Appropriate trade-offs must be sometimes found out.…”

Section: Cold Energy Recovery Considerationmentioning

confidence: 99%

Design of an Intermediate Fluid Vaporizer for Liquefied Natural Gas

Chen

Fan

2017

Chem Eng & Technol

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The intermediate fluid vaporizer (IFV) owns various advantages over other types of liquefied natural gas (LNG) vaporizers. Research findings related to the design of an IFV, including configurational variations, selection of candidate working fluids, one‐dimensional steady‐state thermal model, computational fluid dynamics (CFD) model for thermal dynamic analysis, and cold energy recovery have been reviewed. Further updates to the current one‐dimensional thermal model are needed. A three‐dimensional unsteady CFD model should be established for the instantaneous flow and heat transfer analysis. Integrating the LNG regasification and cold energy recovery will bring more complexity in process design, optimization, and operation.

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“…Lira‐Barragán et al proposed a stochastic MILP model for the synthesis of water networks in shale gas production considering uncertainties corresponding to fracturing water requirement and flowback water generation. Wang and Xu presented a scenario‐based MILP model to guide the process integration of shale gas processing and liquefied natural gas regasification with consideration of the uncertain shale gas feed rate. Although the stochastic programming approach is playing a dominant role in addressing the design and optimization of shale gas supply chains under uncertainty, its shortcomings and limitations are obvious.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

2018

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This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two‐stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first‐stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second‐stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data‐driven approach is applied to construct the ambiguity set based on principal component analysis and first‐order deviation functions. By taking advantage of affine decision rules, a tractable mixed‐integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small‐scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019

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Optimal design and operation for simultaneous shale gas NGL recovery and LNG re-gasification under uncertainties

Cited by 30 publications

References 10 publications

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

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

Design of an Intermediate Fluid Vaporizer for Liquefied Natural Gas

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

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