Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising t...Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.展开更多
The dynamic system control circuit board(DSCCB)is one of the most important components for dynamic system of pure electric vehicles. The current detection of the DSCCB is done manually, which is not only inefficient i...The dynamic system control circuit board(DSCCB)is one of the most important components for dynamic system of pure electric vehicles. The current detection of the DSCCB is done manually, which is not only inefficient in the detection but also difficult to guarantee the data accuracy. In order to improve the detection efficiency and accuracy, a new testing system is designed by Labview. The total test time can be further reduced by about 75% compared with the results of the manual detection. In this paper, the three-parameter sine wave curve-fit algorithm theory is applied to the phase delay detection of the current sensor sampling circuit on the DSCCB. This method solves the problem of big error in the phase delay detection.展开更多
With an advanced foreign hydraulic automatic transmission as the objective,an analytical method for the gear-shifting schedule is proposed.First the demanded maximum gradient of test is estimated.Then a test scheme an...With an advanced foreign hydraulic automatic transmission as the objective,an analytical method for the gear-shifting schedule is proposed.First the demanded maximum gradient of test is estimated.Then a test scheme and analytical procedure is formulated by initial test and hypothetical shift parameters.Finally through gear-shifting tests under different road conditions,load,accelerator pedal position limitation,throttle opening and output shaft speed are found to be the gear-shifting parameters.Under a common road condition,the gear-shifting schedule is a double-parameter schedule.Based on the driver's demands on braking and dynamic performance,different shift schedules are made under downhill,uphill and quick releasing acceleration pedal conditions.The operation criteria of down-shift schedule on abrupt grade are proposed.展开更多
基金funding support from National Natural Science Foundation of China(52074322)Beijing Natural Science Foundation(3204052)+1 种基金Science Foundation of China University of Petroleum,Beijing(No.2462018YJRC032)National Major Project of China(2017ZX05030002-005)。
文摘Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.
基金"863"program-saving and new energy vehicles of major projects funded project(2008AA11A154)
文摘The dynamic system control circuit board(DSCCB)is one of the most important components for dynamic system of pure electric vehicles. The current detection of the DSCCB is done manually, which is not only inefficient in the detection but also difficult to guarantee the data accuracy. In order to improve the detection efficiency and accuracy, a new testing system is designed by Labview. The total test time can be further reduced by about 75% compared with the results of the manual detection. In this paper, the three-parameter sine wave curve-fit algorithm theory is applied to the phase delay detection of the current sensor sampling circuit on the DSCCB. This method solves the problem of big error in the phase delay detection.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA112101)
文摘With an advanced foreign hydraulic automatic transmission as the objective,an analytical method for the gear-shifting schedule is proposed.First the demanded maximum gradient of test is estimated.Then a test scheme and analytical procedure is formulated by initial test and hypothetical shift parameters.Finally through gear-shifting tests under different road conditions,load,accelerator pedal position limitation,throttle opening and output shaft speed are found to be the gear-shifting parameters.Under a common road condition,the gear-shifting schedule is a double-parameter schedule.Based on the driver's demands on braking and dynamic performance,different shift schedules are made under downhill,uphill and quick releasing acceleration pedal conditions.The operation criteria of down-shift schedule on abrupt grade are proposed.
基金国家自然科学基金资助项目(51178469),National Natural Science Foundation of China(51178469)中南大学中央高校基本科研业务费专项资金资助项目(2017ZZTS593),Fundamental Research Funds for the Central Universities of Central South University(2017ZZTS593)国家自然科学基金高速铁路基础研究联合基金资助项目(U1334203,U1134209)