本研究基于对数周期幂律模型LPPL(Log Periodic Power Law Model),针对金融时间序列将一维价格波动翻译成反映市场泡沫微观结构的多维变量。通过对多维变量的动态监测,把握市场中泡沫的演变并预测泡沫破裂的临界点,从而有效降低或防范...本研究基于对数周期幂律模型LPPL(Log Periodic Power Law Model),针对金融时间序列将一维价格波动翻译成反映市场泡沫微观结构的多维变量。通过对多维变量的动态监测,把握市场中泡沫的演变并预测泡沫破裂的临界点,从而有效降低或防范金融资产泡沫破裂所导致的风险。为检验LPPL模型在中国金融市场中的适用性,本文分别使用上证综指、四个期货连续合约以及两支个股检验模型效果。实证结果表明当金融资产价格序列呈现超指数加速震荡上升或下降时,该模型能获得稳定的估计效果,有效预测泡沫破裂临界时点。展开更多
Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural netwo...Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.展开更多
Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined...Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined experimentally.Although,experimental methods present valid and reliable results,they are expensive,time-consuming,and require much care when taking test samples.Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties(e.g.,bubble point pressure);however,these methods have a number of limitations.In the present study,a novel numerical model based on artificial neural network(ANN)is proposed for the prediction of bubble point pressure as a function of solution gaseoil ratio,reservoir temperature,oil gravity(API),and gas specific gravity in petroleum systems.The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world.An optimization process was performed on networks with different structures.Based on the obtained results,a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure.The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gaseoil ratios in the range of 8.61e3298.66 SCF/STB,temperatures between 74 and 341.6F,oil gravity values of 6e56.8 API and gas gravity values between 0.521 and 3.444.The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses.The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.展开更多
文摘本研究基于对数周期幂律模型LPPL(Log Periodic Power Law Model),针对金融时间序列将一维价格波动翻译成反映市场泡沫微观结构的多维变量。通过对多维变量的动态监测,把握市场中泡沫的演变并预测泡沫破裂的临界点,从而有效降低或防范金融资产泡沫破裂所导致的风险。为检验LPPL模型在中国金融市场中的适用性,本文分别使用上证综指、四个期货连续合约以及两支个股检验模型效果。实证结果表明当金融资产价格序列呈现超指数加速震荡上升或下降时,该模型能获得稳定的估计效果,有效预测泡沫破裂临界时点。
文摘Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.
文摘Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined experimentally.Although,experimental methods present valid and reliable results,they are expensive,time-consuming,and require much care when taking test samples.Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties(e.g.,bubble point pressure);however,these methods have a number of limitations.In the present study,a novel numerical model based on artificial neural network(ANN)is proposed for the prediction of bubble point pressure as a function of solution gaseoil ratio,reservoir temperature,oil gravity(API),and gas specific gravity in petroleum systems.The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world.An optimization process was performed on networks with different structures.Based on the obtained results,a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure.The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gaseoil ratios in the range of 8.61e3298.66 SCF/STB,temperatures between 74 and 341.6F,oil gravity values of 6e56.8 API and gas gravity values between 0.521 and 3.444.The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses.The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.
