As for ultra-low permeability reservoir,the adaptability of common nine-spot well pattern is studied through large-scale flat models made by micro-fractured natural sandstone outcrops.Combined with non-linear porous f...As for ultra-low permeability reservoir,the adaptability of common nine-spot well pattern is studied through large-scale flat models made by micro-fractured natural sandstone outcrops.Combined with non-linear porous flow characteristics,the concept of dimensionless pressure sweep efficiency and deliverability index are put forward to evaluate the physical models' well pattern adaptability.Through experiments,the models' pressure distribution is measured and on which basis,the pressure gradient fields are drawn and the porous flow regions of these models are divided into dead oil region,non-linear porous flow region,and quasi-linear porous flow region with the help of twin-core non-linear porous flow curve.The results indicate that rectangular well pattern in fracture reservoirs has the best adaptability,while the worst is inverted nine-spot equilateral well pattern.With the increase of drawdown pressure,dead oil region decreases,pressure sweep efficiency and deliverability index increase; meantime,the deliverability index of rectangular well pattern has much more rational increase.Under the same drawdown pressure,the rectangular well pattern has the largest pressure sweep efficiency.展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
The influence of sand dust on discharge of external insulation has caused widespread concern.At present,the research results show wind-sand electricity has a remarkable effect on the discharge characteristics of insul...The influence of sand dust on discharge of external insulation has caused widespread concern.At present,the research results show wind-sand electricity has a remarkable effect on the discharge characteristics of insulator and has little influence on the discharge characteristics of air gap.The flashover of insulator strings occurs along the insulator surface and air gaps,and the sand dust deposited on the insulator surface may affect the flashover characteristics of insulator strings.This paper studies the flashover characteristics of flat plate model under DC voltage in wind-sand condition.The experimental results show that under positive polarity voltage,the flashover voltage of the flat plate model has a maximum value,while under negative polarity voltage,the flashover voltage of the flat plate model has a minimum value with a certain degree of sand dust deposition.The wind or sand in sand-dust weather has an important effect on the flashover characteristics of the flat plate model.In certain variation range of electric charge,electric charge of sand dust has little effect on the flashover voltage of flat plate model under DC voltage.The deposition of sand has significant influence on the flashover process of flat plate model,which is related to the deposition density and moisture content of sand particle.展开更多
基金Project(2011ZX05013-006)supported by the National Science and Technology Project of China
文摘As for ultra-low permeability reservoir,the adaptability of common nine-spot well pattern is studied through large-scale flat models made by micro-fractured natural sandstone outcrops.Combined with non-linear porous flow characteristics,the concept of dimensionless pressure sweep efficiency and deliverability index are put forward to evaluate the physical models' well pattern adaptability.Through experiments,the models' pressure distribution is measured and on which basis,the pressure gradient fields are drawn and the porous flow regions of these models are divided into dead oil region,non-linear porous flow region,and quasi-linear porous flow region with the help of twin-core non-linear porous flow curve.The results indicate that rectangular well pattern in fracture reservoirs has the best adaptability,while the worst is inverted nine-spot equilateral well pattern.With the increase of drawdown pressure,dead oil region decreases,pressure sweep efficiency and deliverability index increase; meantime,the deliverability index of rectangular well pattern has much more rational increase.Under the same drawdown pressure,the rectangular well pattern has the largest pressure sweep efficiency.
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金Project Supported by National Natural Science Foundation of China（90510014 ）.
文摘The influence of sand dust on discharge of external insulation has caused widespread concern.At present,the research results show wind-sand electricity has a remarkable effect on the discharge characteristics of insulator and has little influence on the discharge characteristics of air gap.The flashover of insulator strings occurs along the insulator surface and air gaps,and the sand dust deposited on the insulator surface may affect the flashover characteristics of insulator strings.This paper studies the flashover characteristics of flat plate model under DC voltage in wind-sand condition.The experimental results show that under positive polarity voltage,the flashover voltage of the flat plate model has a maximum value,while under negative polarity voltage,the flashover voltage of the flat plate model has a minimum value with a certain degree of sand dust deposition.The wind or sand in sand-dust weather has an important effect on the flashover characteristics of the flat plate model.In certain variation range of electric charge,electric charge of sand dust has little effect on the flashover voltage of flat plate model under DC voltage.The deposition of sand has significant influence on the flashover process of flat plate model,which is related to the deposition density and moisture content of sand particle.
