Research on optimization method of deep neural network

2017 ◽  
Author(s):  
Pengfei Liu ◽  
Huaici Zhao ◽  
Feidao Cao
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Optimization Method

scholarly journals Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lihui Tang ◽  
Junjian Li ◽  
Wenming Lu ◽  
Peiqing Lian ◽  
Hao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Deep Neural Network ◽  
Optimization Method ◽  
Dynamic Prediction ◽  
Saturation Field ◽  
Well Control ◽  
Control Optimization ◽  
Calculation Results ◽  
Production Dynamic

A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

scholarly journals An Adaptive Deep Learning Optimization Method Based on Radius of Curvature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiahui Zhang ◽  
Xinhao Yang ◽  
Ke Zhang ◽  
Chenrui Wen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Moving Average ◽  
Optimization Method ◽  
Convergence Speed ◽  
Radius Of Curvature ◽  
Oscillation Problem ◽  
Speed And Accuracy

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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