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| 基于PSO-GRG的背散射模式扫描电镜的数字处理及应用 |
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范卓颖1,林承焰1,于之深2,葛新民1,3,魏肃东4
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(1.中国石油大学地球科学与技术学院,山东青岛 266580;2.渤海钻探工程公司测井分公司勘探开发测井作业部,天津 300280;3.中国石油大学CNPC测井重点实验室,山东青岛 266580;4.中海油田服务股份有限公司,天津 300450)
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| 摘要: |
| 根据背散射扫描电镜中灰度信息的多元高斯分布特征构造目标函数,应用粒子群-广义简约梯度(PSO-GRG)联合优化算法求解岩石不同成分的灰度阈值,结合形态学滤波和图像融合算法得到典型元素图,并定量计算矿物体积分数及孔隙度。结果表明:所得岩石成分信息与氦气法、X衍射法所得结果一致性好,可靠性高;在取柱塞样品困难和样品规则度差的地区具有较好的推广性,可以为定量计算岩石成分信息提供新的手段,充分挖掘背散射模式扫描电镜图像中所蕴含的丰富地质信息。 |
| 关键词: 背散射模式 扫描电镜 PSO-GRG 矿物体积分数 孔隙度 X衍射 |
| DOI：10.3969/j.issn.1673-5005.2015.02.002 |
| 分类号::P 631.84 |
| 基金项目:国家自然科学基金项目(41404086);中国博士科学基金项目(2014M560591);国家科技重大专项(2011ZX05020-008);国家重大科学仪器设备开发专项(2013YQ170463);中国石油天然气集团公司科学研究与技术开发项目(2012E-34-12) |
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| A new method of extracting mineral compositions and pore space from backscatter electron imaging of sandstone based on PSO-GRG algorithm |
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FAN Zhuoying1, LIN Chengyan1, YU Zhishen2, GE Xinmin1,3,WEI Sudong4
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(1.College of Geosciences in China University of Petroleum, Qingdao 266580, China;2.Exploration and Development Department of Logging Operation Wireline Logging Company, BHDC, Tianjing 300280, China;3.CNPC Key Well Logging Laboratory in China University of Petroleum, Qingdao 266580, China;4.China Oilfield Services Limited, Tianjing 300450, China)
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| Abstract: |
| Based on the multi-Gaussian distribution of grey histograms in backscattering electron images, a hybrid method combing the particle swarm optimization and the generalized reduced gradient algorithms (PSO-GRG) was developed to solve the nonlinear objective function, aiming for obtaining the thresholding values of different constituents of the rock. Typical element skeletons were then obtained by morphological filtering-image fusion methods. Thus, volumes of these solid constituents and the porosity could be extracted conveniently. By comparing the calculation results with porosity of helium method as well as mineral content of X-ray diffraction, it shows that the calculation results are reliable. This method can be widely applied to identify the porosity and mineral components with hard-take or random-shape samples which fully takes the advantage of the rich geological information from backscattering electron imaging. |
| Key words: backscattering electron imaging PSO-GRG mineral content porosity X-ray diffraction |
