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本文描述了一种在层段补偿或分配模式中都能够实施的网络优化和控制方法。在补偿模式下的多层合采智能油井系统内,该工艺通过激励高产层同时限制低产层使油井某一特定液流(原油或天然气)的总吞吐量最大化。在层段分配模式下,基于预定的分配系数,运用该技术分配各产油层的产量。这个方法与那些用于资源分配和动态规划中的方法相似。与梯度型的方法不同,这种方法不需要计算微分,它使用历史层段产量、压力/温度数据,以及安装的井下元件的特性来为每个层段的井下控制阀推荐新的作业条件。分析包括油藏边界内层段控制阀的特征,并规定目的井的动态或静态约束条件,然后把该分析与现有的层段数据和全部油井数据相结合,来预测层段控制阀的优化作业条件。这一方法既可用于被动模式,也可用于主动模式,在离散或连续流动过程中同样也可以使用。
This article describes a method of network optimization and control that can be implemented in a layer compensation or allocation mode. In a compensation-mode, multi-ply, smart-well system, the process maximizes the total throughput of a particular well (crude oil or natural gas) in a well by stimulating the high-yielding zone while limiting the low-producing zone. In the layer-by-layer distribution model, the technology is used to allocate the production of each oil-producing layer based on a predetermined distribution coefficient. This method is similar to those used for resource allocation and dynamic planning. Unlike gradient methods, this method does not require calculation of the derivative. It uses historical section production, pressure / temperature data, and the characteristics of the downhole components installed to recommend new operating conditions for the downhole control valve for each interval. Analyze the characteristics of the control valves in the inner section including the reservoir boundary and specify the dynamic or static constraints of the target well and then combine the analysis with the existing interval data and all the well data to predict the optimization of the interval control valve Operating conditions. This method can be used both for passive mode and for active mode, as well as for discrete or continuous flow.