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Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs
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ZHANG Rui,JIA Hu
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Table 4 Forecast results of all producers in well group No. 6.
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Well
name | Actual average
daily output/m3 | Average daily output from
machine learning/m3 | Average daily output from history matching/m3 | Relative error of
machine learning | Relative error of
history matching | | X49-6 | 2.75 | 2.58 | 3.29 | 0.066 | 0.196 | | X50-5-1 | 0.59 | 0.57 | 1.37 | 0.035 | 1.404 | | X50-7-2 | 1.55 | 1.57 | 1.43 | 0.013 | 0.077 | | X51-5-1 | 2.79 | 2.48 | 3.38 | 0.125 | 0.211 | | X51-6 | 3.49 | 3.25 | 3.98 | 0.074 | 0.140 |
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