电成像测井处理及解释方法研究
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摘要
电成像测井以其高分辨率和图像直观性的特点,在复杂油气藏的勘探以及非均质性储层评价等方面得到广泛应用,而电成像测井数据的预处理方法和图像的处理技术关系着电成像测井地质解释的精度和准确性,同时对典型的地质现象建立成像图像模板库并进行自动匹配识别、地质参数的自动定量计算、电成像测井识别地层周期等方面的研究,随着电成像测井在地质解释上的应用深入,越来越受到人们关注。
本论文首先以ERMI所测量的数据为研究对象,进行预处理校正和图像生成与显示方法的研究:着重研究了基于遇卡识别的改进Kalman滤波器模型-加速度校正方法,消除了井下仪器复杂运动引起的图像压缩拉伸和锯齿现象;首次考虑分别从极板内和极板间相结合进行电成像测井数据的均衡化处理,更好地消除了图像上出现的竖条纹和黑色条带现象;针对ERMI测量数据范围较大的问题,提出了基于Guass分布的最佳概率分布函数对成像图像进行直方图规定增强处理,有效地解决了图像上出现的亮带和暗带现象,最终生成的电成像测井图像取得了与斯伦贝谢处理结果相媲美的效果。
在此基础上,研究了从电成像测井图像上自动识别孔洞、裂缝和定量计算相关地质参数的方法:在自动识别孔洞方面,采用最大类间方差法对图像进行自动分割,然后利用Freeman 8-链码存储自动拾取的孔洞轮廓,提高了孔洞参数的计算速度和精度;裂缝识别评价方面,采用了改进的Hough变换与人机交互相结合的方法识别裂缝和定量计算裂缝参数。为了给地质解释提供参照和指导,将典型的成像图像特征与地质现象建立了相对应的解释模板库,利用特征曲线相关性匹配和BP神经网络模板匹配识别方法,自动给出相似度最高的模板,提高了电成像测井解释的可靠性和效率;同时,首次提出利用Blackman-Tukey、MC-CLEAN信号分析方法对电成像测井数据进行频谱分析识别地层周期,比利用常规测井曲线识别的地层周期更为详尽,取得了与米兰科维奇周期匹配一致的结果,为高分辨率地层层序的分析提供了一种新的方法和思路;最后在微软的.NET平台上自主开发了一套电成像测井处理与解释方法软件,成功挂接到中海油田服务公司的测井处理和解释平台上,对现场实测资料进行处理验证,并将处理结果与国内外同类商业软件对比分析,取得了较好的一致效果。
Electrical imaging log with the feature of high resolution and intuitive image has been widely used in the terms of complex reservoir exploration and heterogeneous reservoir evaluation, while the electrical imaging logging data and image processing technology influenced the accuracy and precision of the geological interpretation. Meanwhile the research in these regions-the establishment of imaging template library for typical geological phenomena and automatically matching identification, automatically quantitative calculation of geological parameters, identification the formation cycle through electrical imaging log-have been increasingly paid attention with the further application of electrical imaging log in geological interpretation.
This paper firstly takes the data measured by ERMI as the research object, studying the preprocessing correction as well as the image generation and display method: focusing on improved Kalman filter model-acceleration correction based on stuck identification, eliminating the image compression tension and sawtooth originating from the complex movement of the tools; firstly considerring the equalization process on the electrical imaging log data from the plates and the gap between them, better getting rid of the vertical strips and black bands on the images; for the large range of the ERMI data, putting forward the best probability distribution function, based on Gauss distribution, enhancing the histogram of the image, effectively solving the phenomenon of bright and dark bands on the images and finally generating new images which can compare to the effects fo the Schlumberger results.
Based on these, studying the methods to identify pore, cave and fracture from the electrical imaging data and quantitatively calculate the relevant geological parameters: In the field of automatical identification of pore space, making use of Maximum Interclass Variance to automatically segmenting the images and then automatically picking up the pore space profile by Freeman 8-chain storage which can increase the speed and the accuracy of the calculation. In the field of identification of fracture, introducing the method combinating the improved Hough transform with Human Computer Interaction to identify the fracture and calculate the fracture parameters. In order to provide reference and guid for the geological interpretation, building the interpretation library linking the typical imaging features to the geological phenomena which can improve the efficiency and reliablity of the electrical imaging log interpretation through automatically giving the highest similarity template by using Bp neural network identification method; Meanwhile, proposing Black-Turkey, MC-CLEAN signal analysis method to deal with the electrical imaging log data to identify formation cycle by spectrum analysis, which can provide more detailed stratigraphic cycles than the use of conventional logging data, obtain the results consistent with the Milankovitch cycle and offer a new approach and ideas for the analysis of high resolution sequence stratigraphy; Finally independently developing a set of electrical imaging processing and interpretation software in the platform of .NET successfully attached to the logging processing and interpretation platform in China Ocean Oilfield Services company and achieving the good results by processing and verifying the measured data on-site and comparing the results with these of domestic and foreign similar commercial software so that it can be applied in oilfields.
本论文首先以ERMI所测量的数据为研究对象,进行预处理校正和图像生成与显示方法的研究:着重研究了基于遇卡识别的改进Kalman滤波器模型-加速度校正方法,消除了井下仪器复杂运动引起的图像压缩拉伸和锯齿现象;首次考虑分别从极板内和极板间相结合进行电成像测井数据的均衡化处理,更好地消除了图像上出现的竖条纹和黑色条带现象;针对ERMI测量数据范围较大的问题,提出了基于Guass分布的最佳概率分布函数对成像图像进行直方图规定增强处理,有效地解决了图像上出现的亮带和暗带现象,最终生成的电成像测井图像取得了与斯伦贝谢处理结果相媲美的效果。
在此基础上,研究了从电成像测井图像上自动识别孔洞、裂缝和定量计算相关地质参数的方法:在自动识别孔洞方面,采用最大类间方差法对图像进行自动分割,然后利用Freeman 8-链码存储自动拾取的孔洞轮廓,提高了孔洞参数的计算速度和精度;裂缝识别评价方面,采用了改进的Hough变换与人机交互相结合的方法识别裂缝和定量计算裂缝参数。为了给地质解释提供参照和指导,将典型的成像图像特征与地质现象建立了相对应的解释模板库,利用特征曲线相关性匹配和BP神经网络模板匹配识别方法,自动给出相似度最高的模板,提高了电成像测井解释的可靠性和效率;同时,首次提出利用Blackman-Tukey、MC-CLEAN信号分析方法对电成像测井数据进行频谱分析识别地层周期,比利用常规测井曲线识别的地层周期更为详尽,取得了与米兰科维奇周期匹配一致的结果,为高分辨率地层层序的分析提供了一种新的方法和思路;最后在微软的.NET平台上自主开发了一套电成像测井处理与解释方法软件,成功挂接到中海油田服务公司的测井处理和解释平台上,对现场实测资料进行处理验证,并将处理结果与国内外同类商业软件对比分析,取得了较好的一致效果。
Electrical imaging log with the feature of high resolution and intuitive image has been widely used in the terms of complex reservoir exploration and heterogeneous reservoir evaluation, while the electrical imaging logging data and image processing technology influenced the accuracy and precision of the geological interpretation. Meanwhile the research in these regions-the establishment of imaging template library for typical geological phenomena and automatically matching identification, automatically quantitative calculation of geological parameters, identification the formation cycle through electrical imaging log-have been increasingly paid attention with the further application of electrical imaging log in geological interpretation.
This paper firstly takes the data measured by ERMI as the research object, studying the preprocessing correction as well as the image generation and display method: focusing on improved Kalman filter model-acceleration correction based on stuck identification, eliminating the image compression tension and sawtooth originating from the complex movement of the tools; firstly considerring the equalization process on the electrical imaging log data from the plates and the gap between them, better getting rid of the vertical strips and black bands on the images; for the large range of the ERMI data, putting forward the best probability distribution function, based on Gauss distribution, enhancing the histogram of the image, effectively solving the phenomenon of bright and dark bands on the images and finally generating new images which can compare to the effects fo the Schlumberger results.
Based on these, studying the methods to identify pore, cave and fracture from the electrical imaging data and quantitatively calculate the relevant geological parameters: In the field of automatical identification of pore space, making use of Maximum Interclass Variance to automatically segmenting the images and then automatically picking up the pore space profile by Freeman 8-chain storage which can increase the speed and the accuracy of the calculation. In the field of identification of fracture, introducing the method combinating the improved Hough transform with Human Computer Interaction to identify the fracture and calculate the fracture parameters. In order to provide reference and guid for the geological interpretation, building the interpretation library linking the typical imaging features to the geological phenomena which can improve the efficiency and reliablity of the electrical imaging log interpretation through automatically giving the highest similarity template by using Bp neural network identification method; Meanwhile, proposing Black-Turkey, MC-CLEAN signal analysis method to deal with the electrical imaging log data to identify formation cycle by spectrum analysis, which can provide more detailed stratigraphic cycles than the use of conventional logging data, obtain the results consistent with the Milankovitch cycle and offer a new approach and ideas for the analysis of high resolution sequence stratigraphy; Finally independently developing a set of electrical imaging processing and interpretation software in the platform of .NET successfully attached to the logging processing and interpretation platform in China Ocean Oilfield Services company and achieving the good results by processing and verifying the measured data on-site and comparing the results with these of domestic and foreign similar commercial software so that it can be applied in oilfields.
引文
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