数值模拟方法驱动的数据流过套管电阻率测井技术

过套管电阻率测井在寻找剩余油、油藏动态监测方面是一种重要的测井技术.文中依据大数据流技术原理,建立以Hadoop架构为核心、集成仪器响应模拟、仪器关键参数设计、影响因素分析校正、岩石物理参数获取与测井评价为一体的过套管电阻率测井系统.这将打破了测井方法、仪器制造、测井采集、岩石物理与测井解释之间的部门界限,实现了不同用户实时访问统一的数据库,实时解决仪器刻度、数据采集与校正,以及综合解释中的岩石物理参数的选择等问题.基于"众包算法"实现测井解释专家的知识共享.根据过套管电阻率测井各种模拟方法特点,建立对应全空间、半空间、三层模型、径向分层纵向分层所对应不同地层模型所对应的解析法、传输线方程法、边界元、模式匹配法、有限元法所的优化高效模块,在系统中快速实现仪器响应分析、影响因素校正及仪器刻度等功能.实例表明,基于这种模拟方法驱动的大数据流过套管电阻率测井技术,能从过套管电测井数据源头上保证数据质量,从而准确求取剩余油饱和度,实现过套管测井地层评价,进而为油藏监测提供可靠的保证,最终为油藏开发方案的决策提供可靠依据.

Through-casing formation resistivity logging technique based on data flow and numerical simulation method

The through-casing formation resistivity logging is a unique and important technique in searching for remaining oil and monitoring in reservoir. According to the principle of data flow on big data technology, the characteristics of various model methods for Through-casing formation resistivity logging,a corresponding optimization and efficient model method module corresponding to different formation and well condition were established, so as to quickly realize the instrument response, the correction of influencing factors and the rapid realization of instrument scale coefficient. The analysis method corresponding to the different stratigraphic models corresponding to the whole space, half space, three-layer model and radial layered longitudinal stratification was designed in this system corresponding the transmission line equation method, boundary element, pattern matching method and finite element method individually. The system of through-casing formation resistivity logging have taken as Hadoop architecture as the core,which integrated instrument response simulation, instrument key parameter analysis, influence factor correction analysis, petrophysical analysis and logging evaluation. Thus, that break the departmental boundary between logging method, instrument manufacturing, logging acquisition, rock physics and logging interpretation, and realizes instrument scale, data acquisition and correction, and parameter selection in comprehensive interpretation by accessing the system for different user. The sharing of expert knowledge by "crowdsourcing algorithm", assistant the comprehensive evaluation parameters of the formation. The application example shows that the big data flowing for Through casing formation resistivity logging technology driven by this simulation method can guaranteed the data quality from the source of Through-casing electrical logging, thus achieve the residual oil saturation, formation evaluation in cased hole, that provide a reliable guarantee for reservoir monitoring. Finally, this system can provide a reliable basis for the decision plan of reservoir development.