A Fast POD-Based Method for Predicting Oil and Water Flow in Water-Drive Reservoir
摘要: 以最佳正交分解(POD)技术为基础提出了一种快速预测油藏中油、水流动问题的方法.采用POD技术建立了水驱油藏中油、水两相流动的低阶模型.通过油藏数值模拟方法获得二维水驱油藏模型在时间0～500 d内的压力和含水饱和度的100个样本， 并从样本中提取出一组压力和含水饱和度的POD基函数.当注采参数不断变化后，采用已求得的POD基函数结合低阶模型对新的物理场进行预测.研究结果表明：POD方法能够快速、准确地预测出水驱油藏的压力和含水饱和度场，文中算例给出压力和含水饱和度场的预测误差分别不超过1.2%与1.5%，且计算速度比直接进行油藏数值模拟快50倍以上.Abstract: A fast method based on the proper orthogonal decomposition (POD) technique for predicting oil and water flow in water-drive reservoir was proposed. The reduced order model of oil and water flow in water-drive reservoir was generated with the POD. An ensemble of 100 samples of pressure and water saturation snapshots in the time range of [0 d, 500 d] with an interval step of 5 d for the 2D water-drive reservoir model was obtained through numerical reservoir simulation, and the POD was applied to extract a reduced set of POD basis functions from these snapshots. After the injection and production parameters were changed continuously, the obtained POD basis functions combined with the reduced order model were used to predict the new physical fields. The research results show that fast and accurate predictions can be achieved with the proposed POD-based method, for the given example, the prediction errors of pressure and water saturation are less than 1.2% and 1.5%, respectively. What’s more, this POD-based method is 50 times faster in calculation than the traditional numerical reservoir simulation.
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