为使液压挖掘机在负载多变条件下节能降耗,需使发动机与变量泵系统实现功率匹配。建立发动机外部特性与变量泵调节装置的数学模型,并提出动力系统功率匹配节能控制策略以及实现该策略的模糊控制算法,得到节能控制系统的理论模型。通过采...为使液压挖掘机在负载多变条件下节能降耗,需使发动机与变量泵系统实现功率匹配。建立发动机外部特性与变量泵调节装置的数学模型,并提出动力系统功率匹配节能控制策略以及实现该策略的模糊控制算法,得到节能控制系统的理论模型。通过采用Matlab/Simulink建立发动机与变量泵功率匹配节能控制系统仿真模型,模拟挖掘机在不同负载下节能控制系统的运行状态。将控制算法写入主控制器的电子控制单元(Electronic control unit,ECU),在试验样机上进行试验。模拟仿真和试验结果表明,采用该节能控制算法后,发动机转速能稳定在目标转速附近,并降低燃油消耗率,达到节能效果。展开更多
Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production de...Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.展开更多
文摘为使液压挖掘机在负载多变条件下节能降耗,需使发动机与变量泵系统实现功率匹配。建立发动机外部特性与变量泵调节装置的数学模型,并提出动力系统功率匹配节能控制策略以及实现该策略的模糊控制算法,得到节能控制系统的理论模型。通过采用Matlab/Simulink建立发动机与变量泵功率匹配节能控制系统仿真模型,模拟挖掘机在不同负载下节能控制系统的运行状态。将控制算法写入主控制器的电子控制单元(Electronic control unit,ECU),在试验样机上进行试验。模拟仿真和试验结果表明,采用该节能控制算法后,发动机转速能稳定在目标转速附近,并降低燃油消耗率,达到节能效果。
基金supported by the National Natural Science Foundation of China (Grant No. 61004095F030202)the China Important National Sci-ence & Technology Specific Projects (Grant No. 2008ZX05030-05-002)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No. 09CX05007A)the National Basic Research Program of China (Grant No. 2011CB201000)
文摘Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.
