The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

Song, Chun; Hou, Yuan Bin; Du, Jing Yi

doi:10.4028/www.scientific.net/amm.596.245

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…When compared with other feed-forward ANNs, the radial basis function neural network (RBFNN) is effective and provides the best approximation performance and global optimality [10,11]. The input, hidden, and output layers are the three layers that make up the RBFNN topology, as shown in Figure 2 [12].…”

Section: Neural Network Modelmentioning

confidence: 99%

Kick Prediction Method Based on Artificial Neural Network Model

et al. 2022

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Kick is one of the most important drilling problems, and because its occurrence makes drilling engineering extremely complex, it is essential to predict the possibility of kick as soon as possible. In this study, k-means clustering was combined with four artificial neural networks: regularized RBFNN, generalized RBFNN, GRNN, and PNN, to estimate the kick risk. To reduce data redundancy and normalize the drilling data, which contain kick conditions, k-means clustering was introduced. The output layer weights were then determined using a brute-force search with different Gaussian function widths, resulting in a series of artificial neural networks composed of different clustering samples and different Gaussian function widths. The results showed that the prediction accuracy of regularized RBFNN + k-means model was the highest, that of the GRNN + k-means model was the lowest. The kick prediction accuracy for regularized RBFNN, generalized RBFNN, GRNN, and PNN were 75.90%, 65.20%, 51.70%, and 70.16%, respectively. This method can be used to enhance the speed and accuracy of kick risk prediction in the field while facilitating the use of and advances in risk warning technology for deep and high-temperature and high-pressure wells.

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Section: Neural Network Modelmentioning

confidence: 99%

Kick Prediction Method Based on Artificial Neural Network Model

et al. 2022

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Corrosion analysis of guyed rod used for 500 kV transmission tower

Feng

Chen

Zhang

et al. 2021

J. Phys.: Conf. Ser.

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As an important part of the guyed device of cable stayed tower, guyed rod plays a key role of preventing transmission tower from tilting and toppling. In this paper, the corroded guyed rod of 500kV transmission tower was investigated by means of macro-morphology inspection, chemical composition analysis, scanning electron micrograph analysis, microstructure analysis and energy spectrum analysis. The result showed that high content of corrosive sulfate in the soil near the leg of transmission tower was the main cause of corrosion of the guyed rod.

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Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks

et al. 2021

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Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of oilfields, which is an important basis to ensure the stable production of the oilfield. Due to existing oilfield development index prediction methods being difficult to accurately reflect the complex nonlinear problem in the oil field development process, using the artificial neural network, which can predict the oilfield development index with the function of infinitely close to any non-linear function, will be the most ideal prediction method at present. This article summarizes four commonly used artificial neural networks: the BP neural network, the radial basis neural network, the generalized regression neural network, and the wavelet neural network, and mainly introduces their network structure, function types, calculation process and prediction results. Four kinds of artificial neural networks are optimized through various intelligent algorithms, and the principle and essence of optimization are analyzed. Furthermore, the advantages and disadvantages of the four artificial neural networks are summarized and compared. Finally, based on the application of artificial neural networks in other fields and on existing problems, a future development direction is proposed which can serve as a reference and guide for the research on accurate prediction of oilfield development indicators.

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The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

Cited by 3 publications

References 2 publications

Kick Prediction Method Based on Artificial Neural Network Model

Kick Prediction Method Based on Artificial Neural Network Model

Corrosion analysis of guyed rod used for 500 kV transmission tower

Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks

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