Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. First...Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.展开更多
From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order ...From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order to review the overall situation and provide information on major accidents of coal mines in China, we investigated 26 major coal mining accidents in China between the years of 1949 and 2009 through statistical methods, each of which led to more than 100 fatalities. Statistical characteristics about accident-related factors such as time, death toll, accident reasons, characters and nature of enterprise were analyzed. And some special conclusions have been achieved. For example, although we have made great progress, the safety situation in China coal mining industry is still serious, and the reasons for the mining accidents are all human errors which are not inevitable. Such results may be helpful to prevent major accidents in coal mines. Moreso, based on both the knowledge of other countries which have good safety situation nowadays and the safety management situation of China, we made suggestion on safety management of China coal mining. In conclusion, countermeasures were proposed in accordance with the results of statistical studies and the analyses of problems existed in coal mines, including the perfec- tion of safety supervision organization, the establishment of cooperating agency among government, coal mines and workers, the perfection of safety rules and regulations, the improvement of safety investment, the enhancement of safety training, the development of safety technique, and the development of emer- gency rescue technique and equipment.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and...Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.展开更多
文摘Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.
基金support from the Science and Technology Programming Project of Shandong Provincein China (No. 2010GSF10808)the National Natural Science Foundation of China (No. 51074100)
文摘From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order to review the overall situation and provide information on major accidents of coal mines in China, we investigated 26 major coal mining accidents in China between the years of 1949 and 2009 through statistical methods, each of which led to more than 100 fatalities. Statistical characteristics about accident-related factors such as time, death toll, accident reasons, characters and nature of enterprise were analyzed. And some special conclusions have been achieved. For example, although we have made great progress, the safety situation in China coal mining industry is still serious, and the reasons for the mining accidents are all human errors which are not inevitable. Such results may be helpful to prevent major accidents in coal mines. Moreso, based on both the knowledge of other countries which have good safety situation nowadays and the safety management situation of China, we made suggestion on safety management of China coal mining. In conclusion, countermeasures were proposed in accordance with the results of statistical studies and the analyses of problems existed in coal mines, including the perfec- tion of safety supervision organization, the establishment of cooperating agency among government, coal mines and workers, the perfection of safety rules and regulations, the improvement of safety investment, the enhancement of safety training, the development of safety technique, and the development of emer- gency rescue technique and equipment.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
基金supported by the National High Technology Research and Development Program(863)of China(No.2015AA016101)the National Natural Science Fund(No.61300184)Beijing Nova Program(No.Z151100000315078)
文摘Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.
