一种时变耦合模型在油井产量预测中的应用

 油井产量随时间的变化关系受多种因素的影响，它们之间是一种极其复杂的非线性关系，常规油藏工程研究方法往往受相关参数的不确定性所限，预测误差偏大。分析认为油井产量数据具有时间序列特征，引入相空间重构技术，利用G-P法求出最佳嵌入维数，对油井产量构成的时间序列进行混沌特性识别。在此基础上，利用支持向量机（SVM）方法，构建具有时变特性的混沌-SVM耦合模型，该模型对中、短期的油井产量预测具有很高的精度，在实际应用中，可以不断补充新的历史数据，进行滚动预测，可靠性更高。实例W8-5井的应用效果也表明，预测结果平均相对误差仅为7%，显示出模型具有较好的预测效果和实用价值。

Application of Time Varying Coupling Model in Prediction of Well Production

The variation of the oil well production against time is controlled by several factors, in an extremely complicated nonlinear manner. The conventional petroleum reservoir engineering method suffers often from uncertainty of the correlation parameters and a large prediction error. Our analysis shows that the oil well production data have some features of time series. Therefore, the technique of the phase space reconstruction and the G-P method can used to obtain the optimal embed dimension and then to identify the time series of the well oil production. On this basis, by using the support vector machine method, the chaos-SVM model with time varying character is built,. with a very high precision for short-term well productions. In a real application, we can supplement the new historical data in real time to make the rolling prediction. The example W8-5 well application indicates that the relative error of the forecast results is only 7%, which shows that the coupling model has a good forecast ability and is of practical value.