酸化井产油量时间序列的混沌特征识别

Chaos Identification of Time Series of Acidized Well Oil Production Rate

现有的油气井产量预测方法以传统技术经济预测的惯性原理为基础。混沌理论的产生与发展对这种预测原理提出了挑战。目前有关酸化井产量变化混沌特征识别与预测的相关研究还鲜有公开报道。采用相空间重构和关联维数提取技术,对西部某油田一油井酸化后60 d的产油量时间序列研究发现,此井产油量时间序列的饱和嵌入维数和对应的吸引子维数分别为11和2.64,由此说明该井酸化后的生产系统动态演化规律为高维空间中的奇异吸引子,因而产油量变化表现出混沌特征;并且说明影响该井产油量变化的基本变量为3～11个。从而为单井生产系统动力学模型的建立,特别是人工神经网络预测模型的输入层节点数量的确定提供了定量的参考。

Well production rate is commonly predicted based on the permanence principle of traditional technical economy prediction.However,such a prediction principle has been challenged by the emergence and development of chaos theory.Unfortunately,there is little work has been done on the chaos identification and prediction of acidized well production rate.This paper presents techniques of phase space reconstruction and correlation dimension extraction,by which the 60-day time series of oil production rate from an acidized well in one oil filed in west China is investigated.It is discovered that the correlation dimension of the time series of oil production rate is 11,and the attracter＇s dimension is 2.64.Thus,the attracter is a strange one and the production rate varied chaotically.Furthermore,it indicates that the number of essential variables of the production variation ranges from 3 to 11.The results present quantitative reference to presenting accurate dynamic model of the production system,especially to defining the correct number of input layer nodes in ANN（artificial neural network） models.