油气生产过程综合能耗模型误差补偿方法  

Error Compensation Method for Comprehensive Energy Consumption in Oil and Gas Production Process

作　　者：刘潭[1] 高宪文[1,2] 王丽娜[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]东北大学流程工业综合自动化国家重点实验室,辽宁沈阳110819

出　　处：《信息与控制》2015年第6期648-653,共6页Information and Control

基　　金：国家自然科学基金资助项目(61034005)

摘　　要：为了提高油气生产过程综合能耗模型的预测精度,本文利用高斯混合模型(GMM)对模型进行误差补偿.并针对传统期望最大化(EM)算法对GMM参数估计时易于陷入局部极小值且存在过拟合的问题,将GMM的结构与参数作为整体进行优化,且对EM算法进行如下改进:首先将梯度算子引入到遗传算法(GA)中构成GGA算法,再将GGA与传统EM算法相结合形成GGA-EM算法.因此提出了一种基于GGA-EM算法的GMM模型误差补偿方法.最后,将提出的模型误差补偿方法应用到某采油作业区的一区块油气生产过程中.结果表明该方法可以有效地提高模型的预测精度,为采油过程的优化控制奠定了坚实基础.In order to improve the prediction accuracy of comprehensive energy consumption models for oil and gas production process, we apply the Gaussian mixture model （GMM） to the error compensation of these models. However, when the traditional expectation-maximization algorithm （EM） is used for GMM parameter estimation, it may cause problems with falling into a local minimum and overfitting. In order to address these problems, we optimize the structure and parameters of the GMM as a whole. In addition, we improve the EM algorithm as follows ： first, we introduce a gradient operator into the genetic algorithm （GA） to form a gradientbased hybrid genetic algorithm （GGA）, and then combine the GGA with the EM algorithm to form a GGA-EM algorithm. Next, we propose a GMM model error compensation method based on the GGA-EM algorithm. Final- ly, we apply the proposed GMM model error compensation method to the oil and gas production process in which there is a blockage in the oil-recovery operation area. The results show that this method can effectively improve model prediction accuracy, and lays a solid foundation for the optimal control of oil production process.

关 键 词：油气生产过程 综合能耗 高斯混合模型(GMM) GGA-EM算法 误差补偿 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]