Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

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.

Automated summary

The method utilizes MTS and VAR model for forecasting oil well production. Analysis of historical production data shows that the machine learning model provides more accurate forecasting results, and uncertainty analysis can improve the safety of predictions. The impulse response analysis can evaluate the production contribution of injection wells, guiding adjustments to waterflooding development plans.