The Open Petroleum Engineering Journal

2015, 8 : 363-367
Published online 2015 August 19. DOI: 10.2174/1874834101508010363
Publisher ID: TOPEJ-8-363

Recognition of Oil Shale Based on LIBSVM Optimized by Modified Genetic Algorithm

Qihua Hu , Cong Wang , Xin Zhang and Jingjing Fan
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China.

ABSTRACT

In order to improved the speed, accuracy and generalization of oil shale recognition model with log dada, considering parameters of traditional SVM were chosen by experience, a LIBSVM recognition model with optimized parameters was proposed based genetic algorithm. First of all, all the samples data were processed to double type as LIBSVM tool needing, and the best normalization way was chosen through comparing different accuracies of various normalization ways. Secondly, the fitness value was calculated by the traditional LIBSVM. Finally, parameters C and g were optimized by genetic algorithm according the fitness value. The optimized LIBSVM oil shale recognition model was applied in northern Qaidam basin to identify oil shale, the results show that optimized recognition model is a tool of better generalization ability and the recognition accuracy reaches as much as 97.2806%. According to the popularization effects in the well area of same geology background, this optimized LIBSVM model is the best for now.

Keywords:

Genetic algorithm, LIBSVM, log interpretation, oil shale recognition.