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
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.展开更多
Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of ...Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of development is established. The model is solved by improved simultaneous perturbation stochastic approximation algorithm(SPSA), and an automatic optimization software for liquid production is developed. This model avoids the disadvantage of traditional optimization methods that only focus on the maximum value of mathematics but ignore the production law of oilfield. It has the advantages of high efficiency of calculation, short period and automatic optimization. It can satisfy the automatic optimization of liquid production in later stage of oilfield development. The software was applied in the oilfield development of D oilfield, Ecuador in South America, and realized the automatic optimization of liquid production in the later stage of oilfield development.展开更多
首先回顾了地震概率安全评价(Seismic Probability Safety Assessment,SPSA)方法的发展历史及应用现状;然后基于SPSA的要素,研究了SPSA方法中需要解决的主要问题,重点是地震易损性分析,并根据SPSA的任务描述SPSA流程,主要探讨电厂走访...首先回顾了地震概率安全评价(Seismic Probability Safety Assessment,SPSA)方法的发展历史及应用现状;然后基于SPSA的要素,研究了SPSA方法中需要解决的主要问题,重点是地震易损性分析,并根据SPSA的任务描述SPSA流程,主要探讨电厂走访和继电器评价的方法;最后结合我国概率安全评价(Probability Safety Assessment,PSA)的研究进展、SPSA的基础等实际状况,提出适合我国的SPSA实施路线的建议,以保障核电站地震情况下的安全。展开更多
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.
文摘Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
基金Supported by the China National Science and Technology Major Project(2016ZX05031-001)
文摘Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of development is established. The model is solved by improved simultaneous perturbation stochastic approximation algorithm(SPSA), and an automatic optimization software for liquid production is developed. This model avoids the disadvantage of traditional optimization methods that only focus on the maximum value of mathematics but ignore the production law of oilfield. It has the advantages of high efficiency of calculation, short period and automatic optimization. It can satisfy the automatic optimization of liquid production in later stage of oilfield development. The software was applied in the oilfield development of D oilfield, Ecuador in South America, and realized the automatic optimization of liquid production in the later stage of oilfield development.
