Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
为提高水下目标波达角(direction of arrival,DOA)估计精度,提出一种基于布谷鸟搜索算法的最大似然DOA估计法.该方法将布谷鸟搜索算法中影响布谷鸟搜索路径的多个参数由固定值改为自适应的动态参数,不仅改进了算法的精度,而且大幅度地...为提高水下目标波达角(direction of arrival,DOA)估计精度,提出一种基于布谷鸟搜索算法的最大似然DOA估计法.该方法将布谷鸟搜索算法中影响布谷鸟搜索路径的多个参数由固定值改为自适应的动态参数,不仅改进了算法的精度,而且大幅度地提高算法的收敛速度.应用改进后的布谷鸟搜索算法优化基于分数低阶空时矩阵的最大似然DOA估计函数,使水下目标DOA估计在准确的前提下更加迅速.仿真结果表明,无论是在多途环境下还是无多途环境下,该方法都能有效地对水下目标进行DOA估计,而且具有很高的收敛速度,因此该方法具有一定的实际应用前景.展开更多
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
文摘为提高水下目标波达角(direction of arrival,DOA)估计精度,提出一种基于布谷鸟搜索算法的最大似然DOA估计法.该方法将布谷鸟搜索算法中影响布谷鸟搜索路径的多个参数由固定值改为自适应的动态参数,不仅改进了算法的精度,而且大幅度地提高算法的收敛速度.应用改进后的布谷鸟搜索算法优化基于分数低阶空时矩阵的最大似然DOA估计函数,使水下目标DOA估计在准确的前提下更加迅速.仿真结果表明,无论是在多途环境下还是无多途环境下,该方法都能有效地对水下目标进行DOA估计,而且具有很高的收敛速度,因此该方法具有一定的实际应用前景.
