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首页> 外文期刊>Journal of Petroleum Science & Engineering >A new parameterization method for data assimilation and uncertainty assessment for complex carbonate reservoir models based on cumulative distribution function
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A new parameterization method for data assimilation and uncertainty assessment for complex carbonate reservoir models based on cumulative distribution function

机译：一种新的参数化方法，用于基于累积分布函数的复杂碳酸盐储层模型的数据同化和不确定性评估

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摘要

Data assimilation (also known as history matching) and uncertainty assessment is the process of conditioning reservoir models to dynamic data to improve its production forecast capacity. One of the main challenges of the process is the representation and updating of spatial properties in a geologically consistent way. The process is even more challenging for complex geological systems such as highly channeling reservoirs, fractured systems and super-K layered reservoirs. Therefore, mainly for highly heterogeneous reservoirs, a proper parameterization scheme is crucial to ensure an effective and consistent process. This paper presents a new approach based on cumulative distribution function (CDF) for parameterization of complex geological models focused on layered reservoir with the presence of high permeability zones (super-K). The main innovative aspect of this work is focused on a new sampling procedure based on a cut-off frequency. The proposed method is simple to implement and, at the same time, very robust. It is able to properly represent super-K distribution along the reservoir during the data assimilation process, obtaining good data matches and reducing the uncertainty in the production forecast. The new method, which preserves the prior characteristics of the model, was tested in a complex carbonate reservoir model (UNISIM-II-H benchmark case) built based on a combination of Brazilian Pre-salt characteristics and Ghawar field information available in the literature. Promising results, which indicate the robustness of the method, are shown.
机译：数据同化（也称为历史匹配）和不确定性评估是将库模型调节到动态数据的过程，以改善其生产预测能力。该过程的主要挑战之一是以地质上一致的方式表示空间属性的表示和更新。该过程更具挑战性，对复杂的地质系统，例如高度通道储层，裂缝系统和超级K分层储存器。因此，主要针对高度异构的储层，适当的参数化方案对于确保有效和一致的过程至关重要。本文介绍了一种基于累积分布函数（CDF）的新方法，用于复杂地质模型的参数化，其具有高渗透区（Super-K）的存在。这项工作的主要创新方面专注于基于截止频率的新采样过程。该方法易于实施，同时非常强大。它能够在数据同化过程中正确地代表沿水库的超级K分布，获得良好的数据匹配并降低生产预测中的不确定性。保留了模型的现有特征的新方法在基于文献中的巴西盐预特性和Ghawar现场信息的组合，在复杂的碳酸酯储层模型（Unisim-II-H基准情况）中进行了测试。显示了有希望的结果，表明该方法的稳健性。

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