Data-driven product design evaluation method based on multi-stage artificial neural network

2021 ◽  
Vol 103 ◽  
pp. 107117
Author(s):  
Lei Wang ◽  
Zhengchao Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Product Design ◽  
Evaluation Method ◽  
Data Driven ◽  
Design Evaluation ◽  
Multi Stage ◽  
Artificial Neural

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural

Hybrid Strategy for Product Quality Credit Evaluation Based on Statistics and Artificial Neural Network

2011 ◽  
Vol 361-363 ◽  
pp. 1499-1505 ◽  
Author(s):  
Li Mei Liu ◽  
Heng Qian ◽  
Yong Chao Gao ◽  
Ding Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Product Quality ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Credit System ◽  
Hybrid Strategy ◽  
Credit Evaluation ◽  
Social Credit ◽  
Artificial Neural

In China, quality credit is an important part of the social credit system, and evaluation of quality credit is the key to the construction of quality credit system. In this paper, on the basis of product quality credit factor analysis and evaluation index construction, a hybrid strategy of three stages is proposed according to the different nature of indicators. The emphasis is put on intelligent evaluation model based on statistics and artificial neural network. According to the results of experimental verification, this credit evaluation method shows a high accuracy for the evaluation of quality credit.

scholarly journals Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Well ◽  
Data Driven ◽  
Well Placement ◽  
Heterogeneous Reservoirs ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Well Data ◽  
Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

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