In order to determine the area for oil and gas exploration in China’s north Sichuan basin,we have divided the time during which the Longmenshan foreland basin was formed into five periods,based on the sedimentary res...In order to determine the area for oil and gas exploration in China’s north Sichuan basin,we have divided the time during which the Longmenshan foreland basin was formed into five periods,based on the sedimentary response relationship of the foreland basin to structural evolution: 1) a late Triassic Noric period;2) an early-Middle Jurassic period;3) a late Jurassic to early Cretaceous period;4) a late Cretaceous to Paleogene-Neogene period and 5) the Quaternary period. As well,we analyzed the sedimentary environment and lithologic features of every basin-forming period. The results show that there are several favorable source-reservoir-cap assemblages in our study area,making it a major region for future oil and gas exploration in China’s northern Sichuan basin.展开更多
By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e...By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.展开更多
基金Projects 40772198 and 50678182 supported by the National Natural Science Foundation of China09-3-094 by the Research Fund for Teaching Reform in Institutes of Higher Learning,Chongqing, China
文摘In order to determine the area for oil and gas exploration in China’s north Sichuan basin,we have divided the time during which the Longmenshan foreland basin was formed into five periods,based on the sedimentary response relationship of the foreland basin to structural evolution: 1) a late Triassic Noric period;2) an early-Middle Jurassic period;3) a late Jurassic to early Cretaceous period;4) a late Cretaceous to Paleogene-Neogene period and 5) the Quaternary period. As well,we analyzed the sedimentary environment and lithologic features of every basin-forming period. The results show that there are several favorable source-reservoir-cap assemblages in our study area,making it a major region for future oil and gas exploration in China’s northern Sichuan basin.
基金Project 40772198 supported by the National Natural Science Foundation of China
文摘By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.
