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Performance of the ensemble Kalman filter outside of existing wells for a channelized reservoir

机译:通道化油藏在现有油井之外的集成卡尔曼滤波器的性能

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The ensemble Kalman filter (EnKF) appears to give good results for matching production data at existing wells. However, the predictive power of these models outside of the existing wells is much more uncertain. In this paper, for a channelized reservoir for five different cases with different levels of information the production history is matched using the EnKF. The predictive power of the resulting model is tested for the existing wells and for new wells. The results show a consistent improvement for the predictions at the existing wells after assimilation of the production data, but not for prediction of production at new well locations. The latter depended on the settings of the problem and prior information used. The results also showed that the fit during the history match was not always a good predictor for predictive capabilities of the history match model. This suggests that some form of validation outside of observed wells is essential.
机译:集成卡尔曼滤波器(EnKF)似乎可以很好地匹配现有井的生产数据。但是,这些模型在现有油井之外的预测能力更加不确定。在本文中,对于具有不同信息级别的五个不同案例的通道化储层,使用EnKF来匹配生产历史。测试了所得模型对现有井和新井的预测能力。结果表明,在吸收了生产数据后,现有井的预测值得到了一致的改善,但没有预测新井位置的产量。后者取决于问题的设置和使用的先验信息。结果还表明,历史匹配期间的拟合度并不总是历史匹配模型的预测能力的良好预测指标。这表明在观察井外进行某种形式的验证是必不可少的。

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