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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 5 Predicted results of cumulative outputs of all producers in each well group.
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Well
group No. | Actual cumulative
production/m3 | Cumulative output from machine learning/m3 | Cumulative output from
history matching/m3 | Relative error of
machine learning | Relative error of
history matching | | 1 | 265.1 | 216.7 | 209.9 | 0.183 | 0.208 | | 2 | 201.4 | 232.2 | 236.7 | 0.153 | 0.175 | | 3 | 173.3 | 194.1 | 200.6 | 0.120 | 0.158 | | 5 | 155.6 | 181.0 | 185.3 | 0.163 | 0.191 | | 6 | 117.0 | 104.5 | 134.5 | 0.107 | 0.150 | | 7 | 113.6 | 129.8 | 94.7 | 0.143 | 0.166 | | 8 | 118.9 | 132.5 | 101.5 | 0.114 | 0.146 | | 9 | 89.6 | 71.3 | 109.8 | 0.204 | 0.225 |
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