智能井压力数据分析方法研究
发布时间：2018-09-06 11:23
【摘要】：智能井通过安装井下设备,实施远程监控油井参数和油藏动态,在增加产量和提高油气采收率方面表现出巨大的潜力,因而得到迅速发展和应用。国内外在智能井系统硬件方面的研究已经取得很大进展,但是包括实时数据处理与解释在内的软件系统的研究仍然比较滞后,如何快速有效地进行实时监测数据的处理与解释乃是智能井实现全面智能化亟待解决的问题之一。论文在充分调研国内外研究成果的基础上,深入剖析了智能井的系统组成及关键技术,完成智能井光纤监测传输系统的设计,实现对井下压力数据的实时采集;运用小波分析理论,通过MATLAB软件对智能井光纤监测传输系统采集到的井下压力数据进行降噪、精简等处理,为后续的井下压力数据解释提供更加明确、可靠的信息;结合油气层渗流理论,采用数据曲线拟合的方法研究了井下压力数据解释模型,并将其运用到实际生产数据中进行计算、分析,识别地层渗流类型,解释地层参数的变化规律。本文引入MATLAB—小波分析和MATLAB—数据曲线拟合方法,初步实现了井下压力监测数据的处理与解释,并结合智能实验井的实际生产数据进行了实例分析和验证。充实了智能井实时监测数据处理与解释方面的研究工作,对智能井技术基础研究的进一步发展具有重要意义。
[Abstract]:Through installation of downhole equipment and remote monitoring of oil well parameters and reservoir performance, intelligent wells have shown great potential in increasing production and increasing oil and gas recovery, so they have been rapidly developed and applied. Great progress has been made in the research of intelligent well system hardware at home and abroad, but the research of software system, including real-time data processing and interpretation, is still lagging behind. How to process and interpret real-time monitoring data quickly and effectively is one of the urgent problems to be solved to realize the full intelligence of intelligent wells. Based on the research results at home and abroad, the paper deeply analyzes the system composition and key technology of intelligent well, completes the design of optical fiber monitoring and transmission system of intelligent well, and realizes the real-time acquisition of downhole pressure data. By using wavelet analysis theory and MATLAB software, the downhole pressure data collected by the optical fiber monitoring and transmission system of intelligent wells are processed by noise reduction and simplification, which provides more clear and reliable information for the subsequent interpretation of downhole pressure data. Combined with the theory of oil and gas reservoir percolation, the interpretation model of underground pressure data is studied by the method of data curve fitting, and it is applied to actual production data to calculate, analyze, identify the type of percolation and explain the variation rule of formation parameters. In this paper, MATLAB- wavelet analysis and MATLAB- data curve fitting method are introduced to realize the processing and interpretation of downhole pressure monitoring data, and the actual production data of intelligent experimental well are analyzed and verified. It enriches the research work of real-time monitoring data processing and interpretation of intelligent wells and is of great significance to the further development of the basic research of intelligent wells technology.
【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】：TE928
本文编号：2226201
[Abstract]:Through installation of downhole equipment and remote monitoring of oil well parameters and reservoir performance, intelligent wells have shown great potential in increasing production and increasing oil and gas recovery, so they have been rapidly developed and applied. Great progress has been made in the research of intelligent well system hardware at home and abroad, but the research of software system, including real-time data processing and interpretation, is still lagging behind. How to process and interpret real-time monitoring data quickly and effectively is one of the urgent problems to be solved to realize the full intelligence of intelligent wells. Based on the research results at home and abroad, the paper deeply analyzes the system composition and key technology of intelligent well, completes the design of optical fiber monitoring and transmission system of intelligent well, and realizes the real-time acquisition of downhole pressure data. By using wavelet analysis theory and MATLAB software, the downhole pressure data collected by the optical fiber monitoring and transmission system of intelligent wells are processed by noise reduction and simplification, which provides more clear and reliable information for the subsequent interpretation of downhole pressure data. Combined with the theory of oil and gas reservoir percolation, the interpretation model of underground pressure data is studied by the method of data curve fitting, and it is applied to actual production data to calculate, analyze, identify the type of percolation and explain the variation rule of formation parameters. In this paper, MATLAB- wavelet analysis and MATLAB- data curve fitting method are introduced to realize the processing and interpretation of downhole pressure monitoring data, and the actual production data of intelligent experimental well are analyzed and verified. It enriches the research work of real-time monitoring data processing and interpretation of intelligent wells and is of great significance to the further development of the basic research of intelligent wells technology.
【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】：TE928
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