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Determining the levels and parameters of thief zone based on automatic history matching and fuzzy method

机译:基于自动历史匹配和模糊方法的小偷区域等级和参数确定

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Thief zone which evolves from long-term water flooding, has become a subject of concern for reservoir engineers, as they lead to early water breakthrough in oil producers and uneven sweep around water injectors, thus it is essential to select wells which need to be modified injection or production profiles. This article presents a methodology of determining the levels and parameters of thief zone in different areas of the reservoir on the concept of "based on the information from every grid of reservoir model" by using automatic history matching and fuzzy method. Since characterizing the reservoir uncertainty is crucial to the reservoir description and future performance predictions, automatic history matching using ensemble Kalman filter (EnKF) with covariance localization is first proposed. Then according to theory of logical analysis (TLA), fuzzy analytic hierarchy process (FAHP) and Fuzzy comprehensive evaluation (FCE) method, the system to quantitative evaluation of thief zone is presented, and the reservoir can be graded into three categories of severe thief zone, light thief zone and no thief zone. The methodology has been applied to X oilfield in western North China which has 17 layers from the top to the bottom in the stratigraphy, and the results show that 5 layers exist severe thief zones and the volume of severe thief zones is the largest in layer 32, and there are four wells in this layer that their injection or production profiles must be modified. In addition, the interwell tracer test result shows the proposed methodology is more accurate by comparing with other methods in the references which mainly rely on the properties of single well to determine the levels of thief zone. The proposed approach is more accurate and less manpower needs to identify thief zones, which also providing a strong basis for oilfield development adjustment in high water cut stage. (c) 2015 Published by Elsevier B.V.
机译:由长期注水演变而来的小偷带已成为油藏工程师关注的问题,因为它们导致了采油厂的早期水突破和注水器周围的不均匀吹扫,因此选择需要改造的油井至关重要。注射或生产档案。本文提出了一种基于“基于储层模型每个网格的信息”的概念,通过自动历史匹配和模糊方法确定储层不同区域的贼区水平和参数的方法。由于表征油藏不确定性对油藏描述和未来性能预测至关重要,因此,首先提出了使用带有协方差定位的集成卡尔曼滤波器(EnKF)进行自动历史匹配的方法。然后根据逻辑分析理论(TLA),模糊层次分析法(FAHP)和模糊综合评价法(FCE),提出了对贼区定量评价的系统,可将储层分为三类。区,轻贼区,无贼区。该方法已应用于华北西部X油田,该地层从上到下共17层,结果表明有5层存在重贼区,重贼区数量最大的是32层。 ,并且在此层中有四口井,必须修改其注入或生产剖面。此外,井间示踪剂测试结果表明,与参考文献中的其他方法(主要依靠单井的特性来确定小偷区域的水平)相比,所提出的方法更为准确。提出的方法更加准确,识别小偷区域所需的人力更少,这也为高含水期油田开发调整提供了坚实的基础。 (c)2015年由Elsevier B.V.

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