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.

Settlement Research and Prediction on Soft Clay Foundation

2012 ◽  
Vol 511 ◽  
pp. 83-87
Author(s):  
Xu Chao Shi ◽  
Yi Feng Dong ◽  
Yun Liu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Simple Structure ◽  
Soft Clay ◽  
Support Vector ◽  
Machine Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Long Period ◽  
Practical Tool

Soft clay can be found in many places around the country of China. Along with the rapid economic development in coastal areas, civil engineers could meet many soft soils. These settlements appear quickly and may continue for a long period of time due to the consolidation behavior. This paper investigates the deformation characteristics of soft clay foundation from trial dates. Support Vector Machine model is proposed to predict settlement of soft clay. The settlement forecasting show Support Vector Machine method has advantages in its simple structure excellent capability in studying and good application prospects. The results of this study proves the elasto-viscoplastic model rationality based on laboratory test and have shown the SVM approach has the potential to be a practical tool for predicting settlement of soft clay foundation.

Application of Fuzzy Support Vector Machine in Chalky Rice Identification

2012 ◽  
Vol 507 ◽  
pp. 202-207
Author(s):  
Xiang Li ◽  
Shang Bing Gao ◽  
Ying Quan Chen
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Identification Accuracy ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fuzzy Support Vector Machine ◽  
Machine Method ◽  
Image Detection ◽  
Sample Mean ◽  
Support Vector Machine Method

In order to improve the identification accuracy of fuzzy support vector machine for chalky rice, this paper puts forward a fuzzy support vector machine method based on fuzzy K nearest-neighbor. This method firstly gets a sample center by calculating sample mean aimed at every class sample; and then it calculates the initial membership of sample by calculating the distance between sample and center; finally, it calculates K neighbor points of each sample, calculates the membership of sample according to the fuzzy K neighbor method, and integrates the initial membership with fuzzy K neighbor membership at a certain proportion, to get the ultimate membership values of samples. Combined with image detection problems of rice, verify the validity of this method. Experiments show that this method not only can improve the accuracy of identification but also can improve its speed, with a better result than common fuzzy support vector machine.

Application of SVM in Predicting the Strength of Cement Stabilized Soil

2012 ◽  
Vol 160 ◽  
pp. 313-317
Author(s):  
Xu Chao Shi ◽  
Qi Xia Liu ◽  
Xiu Juan Lv
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Machine Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Modeling ◽  
Vector Machines ◽  
Regression Approach ◽  
Stabilized Soil ◽  
Novel Method

Support Vector Machine is a powerful machine learning technique based on statistical learning theory. This paper investigates the potential of support vector machines based regression approach to model the strength of cement stabilized soil from test dates. Support Vector Machine model is proposed to predict compressive strength of cement stabilized soil. And the effects of selecting kernel function on Support Vector Machine modeling are also analyzed. The results show that the Support Vector Machine is more precise in measuring the strength of cement than traditional methods. The Support Vector Machine method has advantages in its simple structure，excellent capability in studying and good application prospects, also it provide us with a novel method of measuring the strength of cement stabilized soil.

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.

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