Prediction of Oil and Gas Reservoir Properties using Support Vector Machines

2011 ◽  
Author(s):  
Fatai Adesina Anifowose ◽  
AbdlAzeem Oyafemi Ewenla ◽  
Safiriyu Ijiyemi Eludiora
Keyword(s):  
Support Vector Machines ◽  
Oil And Gas ◽  
Support Vector ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Vector Machines ◽  
Oil And Gas Reservoir

scholarly journals Applications of Support Vector Machines in the Exploratory Phase of Petroleum and Natural Gas: a Survey

2013 ◽  
Vol 2 (2) ◽  
pp. 113 ◽  
Author(s):  
Yuri Javier Ccoicca
Keyword(s):  
Support Vector Machines ◽  
Oil And Gas ◽  
Support Vector ◽  
Oil And Gas Exploration ◽  
Data Mining Algorithms ◽  
Research Areas ◽  
Theoretical Approaches ◽  
Vector Machines ◽  
Mining Algorithms ◽  
Exploration Phase

This paper presents an overview of support vector machines (SVM) as one of the most promising intelligent techniques for data analysis found in the published literature, as theoretical approaches and sophisticated applications developed for various research areas and problem domains. This work is an attempt to provide a survey of the applications of SVM for oil and gas exploration to professionals, researchers and academics involved with the hydrocarbons industry. The applications of SVM have been grouped and summarized in the different areas of the exploration phase, which can be used as a guide to assess the effectiveness of SVM over other data mining algorithms. It also provides a better understanding of the various applications that have been developed for an area that offers a glimpse of innovative applications in other domains of the industry.

scholarly journals A support vector machine method for bid/no bid decision making

2017 ◽  
Vol 23 (5) ◽  
pp. 641-649 ◽  
Author(s):  
Rifat SONMEZ ◽  
Burak SÖZGEN
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Support Vector Machine Classifier ◽  
Oil And Gas ◽  
Support Vector ◽  
Machine Method ◽  
Generalization Performance ◽  
Vector Machines ◽  
Input Variables

The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advan­tage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the sup­port vector machines.

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