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A More General Capillary Pressure Curve and Its Estimation From Production Data

机译:一种更通用的毛细管压力曲线及其从生产数据的估算

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Capillary pressure plays a very important role in naturally fractured reservoirs. The exchange of fluids between matrix and fracture is essentially dependent on capillary pressure expressed by the capillary pressure curve. Capillary force may contribute to the displacement process through imbibition process, or may oppose it in the drainage displacement process. It is essential to represent the capillary pressure curves properly. Many capillary pressure correlations have been suggested in the literature. Capillary pressure data were normally measured using core data. However these methods suffer limitation due to the scale over which the data were collected. Therefore it will be very useful if the capillary pressure curve could be extracted from production data through the history matching process. This paper presents a simple and generalized capillary pressure model which captures both the spontaneous and forced parts of the imbibition capillary pressure curve. In addition, the capillary pressure was estimated by the production data. The sensitivities of production data with respect to capillary pressure model parameters are required when the gradient-based optimization algorithm is used to minimize the objective function. How to calculate the sensitivities of the production data with respect to the capillary pressure model parameters is provided. By minimizing the objective function which describes the mismatch of the observed and simulated production data, the water-oil capillary pressure curves are estimated. All of implementations are incorporated into a commercial simulator (ECLIPSE) and iterated in the automatic history matching scheme. The method is validated using many synthetic cases.
机译:毛细管压力在自然骨折的储层中起着非常重要的作用。基质和裂缝之间的流体交换基本上取决于毛细管压力曲线表示的毛细管压力。毛细管力可以通过利用过程有助于位移过程,或者可以在排水位移过程中反对。必须正确代表毛细管压力曲线。在文献中提出了许多毛细管压力相关性。通常使用核心数据测量毛细管压力数据。然而,由于收集了数据的规模,这些方法因收集数据而受到限制。因此,如果可以通过历史匹配过程从生产数据中提取毛细管压力曲线,它将非常有用。本文介绍了一种简单且广泛的毛细管压力模型,其捕获了吸入毛细管压力曲线的自发和强制部件。此外,毛细管压力由生产数据估算。当使用基于梯度的优化算法来最小化目标函数时,需要对毛细管压力模型参数的产生数据相对于毛细管压力模型参数的敏感性。如何计算关于毛细压力模型参数的生产数据的敏感性。通过最小化描述观察和模拟生产数据不匹配的目标函数,估计了水油毛细管压力曲线。所有实现都被纳入了商业模拟器(Eclipse)并在自动历史匹配方案中迭代。使用许多合成案例验证该方法。

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