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Fengqi You - One of the best experts on this subject based on the ideXlab platform.

  • A Data-Driven Optimization Approach for the Optimization of Shale Gas Supply Chains under Uncertainty
    Chemical engineering transactions, 2019
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    This paper 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 as well as second-stage operational decisions. 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 case study of Marcellus shale gas supply chain. Comparisons with deterministic optimization models are investigated as well.

  • Monetizing shale gas to polymers under mixed uncertainty: Stochastic modeling and likelihood analysis
    AIChE Journal, 2018
    Co-Authors: Ming Pan, Fengqi You, Bingjian Zhang, Qinglin Chen, Jingzheng Ren
    Abstract:

    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. © 2017 American Institute of Chemical Engineers AIChE J, 2017

  • risk management of shale gas supply chain under Estimated Ultimate Recovery uncertainty
    Computer-aided chemical engineering, 2016
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    Abstract This paper addresses the risk management for optimal design and operations of shale gas supply chains under uncertainty of Estimated Ultimate Recovery (EUR). A multiobjective two-stage stochastic mixed-integer linear programming model is proposed to optimize the expected total cost and the financial risk. The latter criterion is measured by conditional value-at-risk (CVaR) and downside risk. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. In order to solve this computationally challenging problem, we integrate both the sample average approximation method and the L-shaped method. The proposed model and solution methods are illustrated through a case study based on the Marcellus shale play. According to the optimization results, the stochastic model provides a feasible design for all the scenarios with the lowest expected total cost. Moreover, after risk management, total expected cost increases but the risk of high-cost scenarios is reduced effectively, and the CVaR management shows its advantage over downside risk management in this specific case study.

  • CDC - Stochastic programming approach to optimal design and operations of shale gas supply chain under uncertainty
    2015 54th IEEE Conference on Decision and Control (CDC), 2015
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    In this paper, we propose the first stochastic model addressing the optimal design and operations of the comprehensive shale gas supply chain, where uncertainties of Estimated Ultimate Recovery (EUR) in each shale well are considered. The resulting mixed-integer linear programming (MILP) model covers the well-to-wire life cycle of shale gas, which consists of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. In order to reduce the model size and the number of scenarios, we use a sample average approximation approach to generate scenarios based on the EUR distribution derived from actual historical data. To demonstrate the proposed stochastic model and solution approach, we present a case study based on Marcellus shale play to maximize the total expected profit of this shale gas supply chain network.

Jiyao Gao - One of the best experts on this subject based on the ideXlab platform.

  • A Data-Driven Optimization Approach for the Optimization of Shale Gas Supply Chains under Uncertainty
    Chemical engineering transactions, 2019
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    This paper 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 as well as second-stage operational decisions. 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 case study of Marcellus shale gas supply chain. Comparisons with deterministic optimization models are investigated as well.

  • risk management of shale gas supply chain under Estimated Ultimate Recovery uncertainty
    Computer-aided chemical engineering, 2016
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    Abstract This paper addresses the risk management for optimal design and operations of shale gas supply chains under uncertainty of Estimated Ultimate Recovery (EUR). A multiobjective two-stage stochastic mixed-integer linear programming model is proposed to optimize the expected total cost and the financial risk. The latter criterion is measured by conditional value-at-risk (CVaR) and downside risk. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. In order to solve this computationally challenging problem, we integrate both the sample average approximation method and the L-shaped method. The proposed model and solution methods are illustrated through a case study based on the Marcellus shale play. According to the optimization results, the stochastic model provides a feasible design for all the scenarios with the lowest expected total cost. Moreover, after risk management, total expected cost increases but the risk of high-cost scenarios is reduced effectively, and the CVaR management shows its advantage over downside risk management in this specific case study.

  • CDC - Stochastic programming approach to optimal design and operations of shale gas supply chain under uncertainty
    2015 54th IEEE Conference on Decision and Control (CDC), 2015
    Co-Authors: Jiyao Gao, Fengqi You
    Abstract:

    In this paper, we propose the first stochastic model addressing the optimal design and operations of the comprehensive shale gas supply chain, where uncertainties of Estimated Ultimate Recovery (EUR) in each shale well are considered. The resulting mixed-integer linear programming (MILP) model covers the well-to-wire life cycle of shale gas, which consists of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. In order to reduce the model size and the number of scenarios, we use a sample average approximation approach to generate scenarios based on the EUR distribution derived from actual historical data. To demonstrate the proposed stochastic model and solution approach, we present a case study based on Marcellus shale play to maximize the total expected profit of this shale gas supply chain network.

B. P. J. Williams - One of the best experts on this subject based on the ideXlab platform.

  • The Leman Field, Blocks 49/26, 49/27, 49/28, 53/1, 53/2, UK North Sea
    Geological Society London Memoirs, 1991
    Co-Authors: A. P. Hillier, B. P. J. Williams
    Abstract:

    AbstractDiscovered in 1966 and starting production in 1968, Leman was the second gas field to come into production in the UK sector of the North Sea. It is classified as a giant field with an Estimated Ultimate Recovery of 11 500 BCF of gas in the aeolian dune sands of the Rotliegend Group. The field extends over five blocks and is being developed by two groups with Shell and Amoco being the operators. Despite being such an old field development drilling is still ongoing in the field with the less permeable northwest area currently being developed.

P. A. Carnicero - One of the best experts on this subject based on the ideXlab platform.

  • The Captain Field, Block 13/22a, UK North Sea
    Geological Society London Memoirs, 2020
    Co-Authors: B. Hodgins, D. J. Moy, P. A. Carnicero
    Abstract:

    Abstract The Captain Field in Block 13/22a is in the Moray Firth region of the UK North Sea. The primary reservoirs are Lower Cretaceous turbidite sandstones of the Captain Sandstone Member. Upper Jurassic shallower-marine Heather Formation sandstones of Oxfordian age provide a secondary reservoir. Total oil in place exceeds 1 Bbbl; however, the oil is heavy and viscous, requiring the continuous application of innovative technologies to maximize economic Recovery from the field. Captain has been producing since 1997, with reservoir waterflood planned from the outset. Captain has been developed using long horizontal producers to maximize reservoir contact. Water injectors provide pressure support, with the aim of full voidage replacement. The Captain development has been phased with facilities consisting of two bridge-linked platforms, a floating production, storage and offloading vessel, and two subsea manifolds. Peak oil rate (100 000 boepd) was achieved in 2002. Average production in 2019 was 28 000 boepd. Captain is executing a chemical enhanced oil Recovery (EOR) project, a first for the UK North Sea. Conventional waterflood yields an Estimated Ultimate Recovery of 30–40%. Chemical EOR is expected to improve this by 5–20% in areas of the reservoir under polymer flood.

Yizhong Chen - One of the best experts on this subject based on the ideXlab platform.

  • impact of unconventional natural gas development on regional water resources and market supply in china from the perspective of game analysis
    Energy Policy, 2020
    Co-Authors: Yizhong Chen, Jing Li, Hongwei Lu, Yiyang Yang
    Abstract:

    Abstract This study presents a detailed framework for evaluating water resource constraint and game-based supply strategies of unconventional and conventional natural gases in China. Surface-water production pressure and groundwater-pollution risk level are used to measure the extent of water resource constraint on shale gas development. Cournot and Stackelberg game models are applied to illustrate the oligopoly mechanism between unconventional and conventional natural gas manufacturing. A subsidy multiplier model is then proposed to identify the impact of government subsidy on different game strategies. Results indicate that high amounts of Estimated Ultimate Recovery and recycling wastewater would significantly reduce water resources consumption during shale gas development. An increased subsidy coefficient would increase the amount of unconventional natural gas but decrease the amount of conventional natural gas. However, the majority of subsidy multiplier values would be less than 1, implying an inconspicuous effect of the subsidy. Moreover, the equilibrium production of unconventional natural gas in the Cournot and Stackelberg game models would increase with the increased substitution factor m1 but decrease with the increased substitution factor m2. Conversely, the high amount of equilibrium production of conventional natural gas would increase the substitution factor m2 but reduce the substitution factor m1 within these two models.

  • Energy-environmental implications of shale gas extraction with considering a stochastic decentralized structure
    Fuel, 2018
    Co-Authors: Yizhong Chen, Honghai Zhao
    Abstract:

    Abstract Major challenges in the shale gas supply chains are identification of the relationship between different stakeholders and evaluation of the environmental impacts under uncertainties. This study develops a stochastic decentralized fractional programming (SDFP) for the life cycle shale gas energy system planning, where the downstream optimization problem is treated as the upper-level model, and the upstream optimization problem is formulated as the lower-level model. Stochastic uncertainties in the Estimated Ultimate Recovery (EUR) and greenhouse gas (GHG) emissions are considered into the decision making process. A SDFP based energy and environmental workflow is then formulated for a real-work case study of Marcellus shale play in Beaver County. Design and operational decisions for both leader and follower are generated in a sequential manner, involving well drilling schedule, energy flows, water resources management, and GHG emissions control. Results reveal that a higher certainty level of EUR value would correspond to a higher reliably in shale gas production, then to increased GHG emissions and economic benefits. Compared with the decentralized linear programs, the SDFP would provide more sustainable strategies, while the linear programs would generate either environment-oriented or economics-oriented strategies. These findings can help stakeholders to achieve the overall satisfaction of the supply chains and to provide useful information for regional GHG emissions control.