Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the...Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.展开更多
A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting...A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization （IMOPSO） is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions （A, B, C） of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ）, IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems.展开更多
The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. T...The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.展开更多
This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and...This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and oil extraction,once constrained by complex geological and petrophysical challenges,has benefited significantly from AI methodologies,which enhance reservoir characterization,fracture prediction,production forecasting,and optimization of hydraulic fracturing.The review examines the current state of AI applications,identifying key advances in AI-driven models that improve predictive accuracy,address data heterogeneity,and integrate diverse data sources.Special attention is given to the combination of AI with traditional physical simulation models,such as physics-informed neural networks,and the potential for hybrid modeling approaches.Despite these advancements,the review highlights several challenges,including data sparsity,model interpretability,and the need for continuous updates with real-time data.Moreover,the review discusses emerging trends,such as explainable AI and real-time monitoring systems,which offer the promise of improving model transparency and adaptability.Future research directions are proposed to enhance the scalability and robustness of AI models,particularly through the integration of advanced hybrid models,uncertainty quantification techniques,and collaborative efforts across academia and industry,ultimately aiming to foster sustainable and efficient shale resource management.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:U2243228The Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention,Grant/Award Number:2022nkms04+1 种基金MOE(Ministry of Education in China)Liberal Arts and Social Sciences Foundation,Grant/Award Number:23YJCZH332Natural Science Foundation of Anhui Province,Grant/Award Numbers:2208085US03,2308085US13。
文摘Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.
基金National Science Fund for Distinguished Young Scholars (No.50725929)National Natural Science Foundation ofChina (No.50539060,50679052)
文摘A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization （IMOPSO） is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions （A, B, C） of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ）, IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems.
基金the Important National Science & Technology Specific Projects of China (Grant No. 2011ZX05024-004)the Natural Science Foundation for Distinguished Young Scholars of Shandong Province, China (Grant No. JQ201115)+2 种基金the Program for New Century Excellent Talents in University (Grant No. NCET-11-0734)the Fundamental Research Funds for the Central Universities (Grant No. 13CX05007A, 13CX05016A)the Program for Changjiang Scholars and Innovative Research Team in University (IRT1294)
文摘The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.
基金supported by the Fuling Shale Gas Environmental Exploration Technology of National Science and Technology Special Project(No.2016ZX05060)the following projects:the“Efficient and Low-Carbon Green Resource Utilization Technology Development of Typical Mine Tailings in the Danjiangkou Water Source Area”(No.20231h0168)the“Key Technology Development for Low-Carbon and High-Value Resource Utilization of Oil-containing Urban Mining in the Danjiangkou Water Source Area”(No.2024BEB027)
文摘This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and oil extraction,once constrained by complex geological and petrophysical challenges,has benefited significantly from AI methodologies,which enhance reservoir characterization,fracture prediction,production forecasting,and optimization of hydraulic fracturing.The review examines the current state of AI applications,identifying key advances in AI-driven models that improve predictive accuracy,address data heterogeneity,and integrate diverse data sources.Special attention is given to the combination of AI with traditional physical simulation models,such as physics-informed neural networks,and the potential for hybrid modeling approaches.Despite these advancements,the review highlights several challenges,including data sparsity,model interpretability,and the need for continuous updates with real-time data.Moreover,the review discusses emerging trends,such as explainable AI and real-time monitoring systems,which offer the promise of improving model transparency and adaptability.Future research directions are proposed to enhance the scalability and robustness of AI models,particularly through the integration of advanced hybrid models,uncertainty quantification techniques,and collaborative efforts across academia and industry,ultimately aiming to foster sustainable and efficient shale resource management.
