[HTML][HTML] Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty
Production optimization has gained increasing attention from the smart oilfield community
because it can increase economic benefits and oil recovery substantially. While existing …
because it can increase economic benefits and oil recovery substantially. While existing …
Deep reinforcement learning and adaptive policy transfer for generalizable well control optimization
Well control optimization is a challenging task but plays a critical role in reservoir
management. Traditional methods independently solve each task from scratch and the …
management. Traditional methods independently solve each task from scratch and the …
Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization
Surrogate-assisted evolutionary algorithms (SAEAs) with prescreening model management
strategies show great potential in handling expensive optimization problems (EOPs) …
strategies show great potential in handling expensive optimization problems (EOPs) …
基于优化-自适应稀疏网格替代模型的地下水模拟参数不确定性分析
李小兰， 曾献奎， 王栋， 吴吉春 - 吉林大学学报(地球科学版), 2023 - xuebao.jlu.edu.cn
地下水模型常用的参数识别方法通常需要多次调用原始模型, 从而导致计算负荷问题.
替代模型是常用来解决模型计算负荷的有效方法, 其中稀疏网格(SG) 替代技术已被广泛应用 …
替代模型是常用来解决模型计算负荷的有效方法, 其中稀疏网格(SG) 替代技术已被广泛应用 …
Fracture network characterization with deep generative model based stochastic inversion
The characterization of fracture networks is challenging for enhanced geothermal systems,
yet is crucial for the understanding of the thermal distributions, and the behaviors of flow field …
yet is crucial for the understanding of the thermal distributions, and the behaviors of flow field …
Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization
Well placement and control scheme optimization is crucial for hydrocarbon, groundwater
and geothermal development, and generally involves a large number of discrete and …
and geothermal development, and generally involves a large number of discrete and …
Evolutionary optimization methods for high-dimensional expensive problems: A survey
Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …
successfully used to solve various real-world optimization problems. The past decade has …
A novel hermit crab optimization algorithm
J Guo, G Zhou, K Yan, B Shi, Y Di, Y Sato - Scientific Reports, 2023 - nature.com
High-dimensional optimization has numerous potential applications in both academia and
industry. It is a major challenge for optimization algorithms to generate very accurate …
industry. It is a major challenge for optimization algorithms to generate very accurate …
Robust optimization of geoenergy production using data-driven deep recurrent auto-encoder and fully-connected neural network proxy
C Xiao, S Zhang, Y Hu, X Gu, X Ma, T Zhou… - Expert Systems with …, 2024 - Elsevier
Robust and efficient optimization of post-history well production schedule under history-
matched geomodel known as closed-loop production management is crucial to achieve …
matched geomodel known as closed-loop production management is crucial to achieve …
Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains
Supply chain digital twin has emerged as a powerful tool in studying the behavior of an
actual supply chain. However, most studies in the field of supply chain digital twin have only …
actual supply chain. However, most studies in the field of supply chain digital twin have only …