针对常见历史拟合方法存在计算量大、油藏参数更新异常、油藏模型修正失真等问题。采用集合平滑算法,通过引入集合卡尔曼滤波算法(EnKF)中多次迭代思路,对相同数据重复吸收,推导出多次数据吸收集合平滑算法(ES-MDA)的核心公式,并编写了自动油藏历史拟合软件。以北海布伦特油田海相砂岩油藏为例,将基于ES-MDA算法的油藏自动历史拟合程序应用于该油藏,对油田的注水采油开发进行历史拟合。结果表明:油藏数值模拟的预测数据与实际测量的数据匹配程度达到90%以上,且能够较准确地表征真实油藏的孔隙度分布特征;ES-MDA算法具有算法稳定、运行效率高、模型更新准确等优点。研究成果对实现计算机自动油藏历史拟合,实时优化油藏生产具有重要意义。
To address the shortcomings of common history matching methods such as large amounts of computation, abnormal update of reservoir parameters, and distortion of reservoir model corrections, ensemble smoother algorithm was adopted to repeatedly assimilate the same data by multiple iterations in the ensemble Kalman filter (EnKF) algorithm to derive the core formula of the ES-MDA algorithm (ensemble smoother with multiple data assimilations) and write automatic reservoir history matching software. In a case study of marine sandstone reservoir of Brent Oilfield in the North Sea, the automatic history matching program of the reservoir based on the ES-MDA was applied to the reservoir to conduct history matching for water injection, oil production and development of the oilfield. The results showed that the predicted data obtained from the numerical modeling of the reservoir matched the actual measured data by more than 90%, and characterized the porosity distribution of the real reservoir more accurately. The ES-MDA algorithm is advantaged by stable algorithm, high operation efficiency and accurate model updating. The research results are of great significance for realizing computer-based automatic reservoir history matching and real-time optimization of reservoir production.
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