Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abunda...Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abundance of organic matter. Using the lower third member of the Shahejie Formation, Zhanhua Sag, Jiyang Depression as the target lithology, we applied core description, thin section observations, electron microscopy imaging, nuclear magnetic resonance, and fullbore formation microimager （FMI） to study the mud shale lithofacies and features. First, the lithofacies were classified by considering the bedding structure, lithology, and organic matter and then a lithofacies classification scheme of lacustrine mud shale was proposed. Second, we used optimal filtering of logging data to distinguish the lithologies. Because the fractals of logging data are good indicators of the bedding structure, gamma-ray radiation was used to optimize the structural identification. Total organic carbon content （TOC） and pyrolyzed hydrocarbons （S2） were calculated from the logging data, and the hydrogen index （HI） was obtained to identify the organic matter type of the different strata （HI vs Tmax）. Finally, a method for shale lithofacies identification based on logging data is proposed for exploring mud shale reservoirs and sweet spots from continuous wellbore profiles.展开更多
Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice pape...Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.展开更多
基金This work was supported by the National Natural Science Foundation of China （Nos. 41202110 and 51674211） and Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation （Southwest Petroleum University） （No. PLN201612）, the Applied Basic Research Projects in Sichuan Province （No. 2015JY0200） and the Open Fund Project from Sichuan Key Laboratory of Natural Gas Geology （No. 2015trqdz07）.
文摘Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abundance of organic matter. Using the lower third member of the Shahejie Formation, Zhanhua Sag, Jiyang Depression as the target lithology, we applied core description, thin section observations, electron microscopy imaging, nuclear magnetic resonance, and fullbore formation microimager （FMI） to study the mud shale lithofacies and features. First, the lithofacies were classified by considering the bedding structure, lithology, and organic matter and then a lithofacies classification scheme of lacustrine mud shale was proposed. Second, we used optimal filtering of logging data to distinguish the lithologies. Because the fractals of logging data are good indicators of the bedding structure, gamma-ray radiation was used to optimize the structural identification. Total organic carbon content （TOC） and pyrolyzed hydrocarbons （S2） were calculated from the logging data, and the hydrogen index （HI） was obtained to identify the organic matter type of the different strata （HI vs Tmax）. Finally, a method for shale lithofacies identification based on logging data is proposed for exploring mud shale reservoirs and sweet spots from continuous wellbore profiles.
基金University-Industry-Science Partnership Project of Guangdong Province and Ministry of Education,China(No.2012B091000155)Strategic Emerging Industries Project of Guangdong Province(No.2011912027)
文摘Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.
