基于智能套管套后测井技术研究

发布时间：2018-05-02 11:04

  本文选题：剩余油饱和度 + 剩余油探测评价方法　； 参考：《西安石油大学》2015年硕士论文

【摘要】：目前,基于套管井评价剩余油饱和度的主要方法有中子寿命测井、碳氧比测井、脉冲中子-中子测井、过套管电阻率测井等。然而,使用这些方法进行解释应用时,还需结合邻井的注水情况,以及其他资料等,以确定套管外的水淹层位为主,定量地确定储层剩余油饱和度。因此,这些方法都有其适用性和局限性。本文从查阅关于剩余油探测评价方法的资料入手,研究基于套管井评价剩余油饱和度方法的适用性和局限性,提出了智能套管测井系统,可以有效的改善上述方法的局限性。本文将详细讲述智能套管的设计构架、智能套管测井原理与方法、智能套管测井仪器框架设计；重点介绍基于COMSOL Multiphysics软件对智能套管测井系统的仿真、稳定电流场理论、分析智能套管测井系统在电场中的分布响应,以及测量地层视电阻率等,以仿真实验论证了智能套管测井系统优越性,为进一步研究智能套管测井系统提供了重要的理论依据。
[Abstract]:At present, the main methods for evaluating residual oil saturation based on casing wells include neutron lifetime logging, carbon-oxygen ratio logging, pulse neutron neutron logging, casing resistivity logging and so on. However, when these methods are used for interpretation and application, it is necessary to combine the water injection situation of adjacent wells, as well as other data, in order to determine the water-flooded layer outside the casing and quantitatively determine the remaining oil saturation of the reservoir. Therefore, these methods have their applicability and limitations. In this paper, the applicability and limitation of the method for evaluating residual oil saturation based on casing wells are studied by consulting the data of residual oil exploration and evaluation methods, and an intelligent casing logging system is proposed, which can effectively improve the limitations of the above methods. In this paper, the design framework of intelligent casing, the principle and method of intelligent casing logging, the framework design of intelligent casing logging tool, the simulation of intelligent casing logging system based on COMSOL Multiphysics software, and the theory of stabilizing current field are introduced in detail. The distribution response of intelligent casing logging system in electric field and the measurement of formation apparent resistivity are analyzed. The superiority of intelligent casing logging system is demonstrated by simulation experiments, which provides an important theoretical basis for further research on intelligent casing logging system.
【学位授予单位】：西安石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P631.81
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本文编号：1833608