scholarly journals Using a support vector machine method to predict the development indices of very high water cut oilfields

2010 ◽  
Vol 7 (3) ◽  
pp. 379-384 ◽  
Author(s):  
Yihua Zhong ◽  
Lei Zhao ◽  
Zhibin Liu ◽  
Yao Xu ◽  
Rong Li
Keyword(s):  
Support Vector Machine ◽  
High Water ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
High Water Cut ◽  
Water Cut ◽  
Very High

scholarly journals Identification of the Thief Zone Using a Support Vector Machine Method

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 373
Author(s):  
Cheng Fu ◽  
Tianyue Guo ◽  
Chongjiang Liu ◽  
Ying Wang ◽  
Bin Huang
Keyword(s):  
Support Vector Machine ◽  
High Water ◽  
Identification Accuracy ◽  
Support Vector ◽  
Machine Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
High Water Cut ◽  
Water Cut ◽  
Thief Zone

Waterflooding is less effective at expanding reservoir production due to interwell thief zones. The thief zones may form during high water cut periods in the case of interconnected injectors and producers or lead to a total loss of injector fluid. We propose to identify the thief zone by using a support vector machine method. Considering the geological factors and development factors of the formation of the thief zone, the signal-to-noise ratio and correlation analysis method were used to select the relevant evaluation indices of the thief zone. The selected evaluation indices of the thief zone were taken as the input of the support vector machine model, and the corresponding recognition results of the thief zone were taken as the output of the support vector machine model. Through the training and learning of sample sets, the response relationship between thief zone and evaluation indices was determined. This method was used to identify 82 well groups in M oilfield, and the identification results were verified by a tracer monitoring method. The total identification accuracy was 89.02%, the positive sample identification accuracy was 92%, and the negative sample identification accuracy was 84.375%. The identification method easily obtains data, is easy to operate, has high identification accuracy, and can provide certain reference value for the formulation of profile control and water shutoff schemes in high water cut periods of oil reservoirs.

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

Urban water resources allocation and shortage risk mapping with support vector machine method

2015 ◽  
Vol 81 (2) ◽  
pp. 1209-1228 ◽  
Author(s):  
Qian Zhang ◽  
Xiujuan Liang ◽  
Zhang Fang ◽  
Tao Jiang ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Risk Mapping ◽  
Support Vector ◽  
Resources Allocation ◽  
Urban Water ◽  
Water Resources Allocation ◽  
Machine Method ◽  
Support Vector Machine Method
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