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

ZHANG Rui,JIA Hu

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Petroleum Exploration and Development ›› 2021, Vol. 48 ›› Issue (1) : 201-211. DOI: 10.1016/S1876-3804(21)60016-2

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

  • ZHANG Rui,JIA Hu()
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Abstract

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.

Key words

waterflooding reservoir / production prediction / machine learning / multivariate time series / vector autoregression / uncertainty analysis

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ZHANG Rui,JIA Hu. Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs. Petroleum Exploration and Development. 2021, 48(1): 201-211 https://doi.org/10.1016/S1876-3804(21)60016-2

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Funding

Huo Yingdong Education Foundation Young Teachers Fund for Higher Education Institutions(171043);Sichuan Outstanding Young Science and Technology Talent Project(2019JDJQ0036)
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