A forecasting method of oil well production based on multivariate time series (MTS) and vector autoregressive (VAR) machine learning model for waterflooding reservoir is proposed, and an example application is carried out. This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis. The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series. Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting. The analysis of history production data of waterflooding reservoirs shows that, compared with history matching results of numerical reservoir simulation, the production forecasting results from the machine learning model are more accurate, and uncertainty analysis can improve the safety of forecasting results. Furthermore, impulse response analysis can evaluate the oil production contribution of the injection well, which can provide theoretical guidance for adjustment of waterflooding development plan.

提出了基于多元时间序列（MTS）和矢量自回归（VAR）机器学习模型的注水油井产量预测方法，并进行了实例应用。该方法首先使用MTS分析在井网分析的基础上优化注入和生产数据。井组中不同生产井的采油量和注水井的注水量被认为是相互关联的时间序列。然后建立一个VAR模型，从MTS​​数据中挖掘线性关系，并通过模型拟合预测油井产量。对注水油藏历史生产数据的分析表明，与数值油藏模拟的历史拟合结果相比，机器学习模型的生产预测结果更加准确，不确定性分析可以提高预测结果的安全性。此外，冲激响应分析可以评价注水井的产油量，为调整注水开发方案提供理论指导。