基于支持向量机的川中杂卤石分类识别研究

doi:10.11867/j.issn.1001-8166.2016.10.1041

地球科学进展

2016, Vol. 31

Issue (10): 1041-1046 DOI: 10.11867/j.issn.1001-8166.2016.10.1041

研究论文

基于支持向量机的川中杂卤石分类识别研究

陈科贵¹, 吴刘磊^{1*, *}, 陈愿愿², 王刚³

1.西南石油大学地球科学与技术学院,四川成都 610500;
2.川庆钻探工程有限公司地球物理勘探公司,四川成都 610213;
3.中国石油新疆油田分公司勘探开发研究院,新疆克拉玛依 834000

Classification and Recognition of Polyhalite in Chuanzhong Based on Support Vector Machine

Chen Kegui¹, Wu Liulei^{1, *}, Chen Yuanyuan², Wang Gang³

1.School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;
2.Geophysical Exploration Company, Chuanqing Drilling Engineering Company Limited, Chengdu 610213, China;
3.Research Institute of Exploration and Development, PetroChina Xinjiang Oilfield Company, Karamay 834000,China

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摘要： 杂卤石是四川盆地主要的固态钾矿物,川中地区大多数杂卤石层不纯,通常伴随石膏层、硬石膏层、盐岩层发育,甚至同层沉积,常规测井解释方法只能粗略地识别杂卤石层。以支持向量机理论和测井解释为基础,测井数据作为输入,构建预测模型,对川中地区下中三叠统杂卤石样本做精细识别,将识别结果与录井资料验证对比,正确率达到90%以上。再以预测模型为基础,结合含杂卤石岩性在测井曲线上的响应情况,构建杂卤石层分类识别模型,识别杂卤石层、石膏质杂卤石层和杂卤石膏岩层,识别正确率达到91.78%,与常规测井解释方法相比具有明显优势。结果表明,将支持向量机运用到找钾矿中具有广阔的前景。

关键词： 支持向量机; 测井响应; 杂卤石; 分类识别

Abstract: Polyhalite is mainly solid mineral potassium in Sichuan Basin, The most of polyhalite layer in Sichuan region is impurity, and usually accompanied by layers of gypsum, anhydrite, rock salt, and even deposited in the same layer. Conventional logging interpretation method can only roughly identificate polyhalite layers. Based on the theory of Support Vector Machine and logging interpretation methods,this paper creates prediction model with the input of logging curves, and discriminates the polyhalite reservoirs in the lower-middle Triassic strata. Compared with logging data, the accuracy rate of the discrimination results reaches 90%. According to the prediction model, identification model can be established with the curve features of polyhalite to discriminate pure polyhalite reservoirs, gypsiferous polyhalite reservoirs and polyhalite-gypsum reservoirs, the accuracy rate is 91.78%. The study demonstrates that Support Vector Machine is superior to the method of logging interpretation, and it has broad prospects in potash exploration.

Key words: Ployhalite Logging response Classified discrimination. Support Vector Machine

收稿日期: 2016-07-18 出版日期: 2016-10-20

P619.21+1

基金资助: 国家自然科学基金项目“四川盆地油钾兼探的地球物理评价方法研究”(编号:41372103); 国家重点基础研究发展计划项目“四川三叠纪古特提斯海盆钾分布、评价研究”(编号:2011CB403002)资助

通讯作者: 吴刘磊(1992-),男,江苏如皋人,硕士研究生,主要从事测井解释工作.E-mail:1556883301@qq.com

作者简介: 陈科贵(1959-),男,四川自贡人,教授,主要从事石油地质、测井储层评价技术和四川钾盐普查研究.E-mail:chenkegui@21cn.com

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引用本文:

陈科贵, 吴刘磊, 陈愿愿, 王刚. 基于支持向量机的川中杂卤石分类识别研究[J]. 地球科学进展, 2016, 31(10): 1041-1046.

Chen Kegui, Wu Liulei, Chen Yuanyuan, Wang Gang. Classification and Recognition of Polyhalite in Chuanzhong Based on Support Vector Machine. Advances in Earth Science, 2016, 31(10): 1041-1046.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2016.10.1041 或 http://www.adearth.ac.cn/CN/Y2016/V31/I10/1041

[1] Huang Xuanzhen. Polyhalite—An ideal potash[C]∥Non-metallic Mineral Materials, Environmental Protection, Ecology, Health and Symposium.Chinese Ceramic Society Professional Committee of Non-metallic Minerals and National Building Materials Industry Committee on Science and Technology Education,2003:7.
∥非金属矿物材料与环保、生态、健康研讨会论文集.中国硅酸盐学会非金属矿专业委员会、国家建材工业科技教育委员会,2003:7.]
[2] Chen Kegui, Li Chunmei, Li Li, et al . Geophysical logging criteria and discriminant model for the potassium-rich strata and their application to Sichuan Basin: A case study of Guang’an area of central Sichuan[J]. Acta Geoscientica Sinica ,2013,31(5):623-630.
. 地球学报,2013,34(5): 623-630.]
[3] Chen Kegui, Li Chunmei, Zhang Jie, et al .Calculating the apparent content of K 2 O with logging data[J]. Journal of Oil and Gas Technology ,2012,34(3):96-100,167.
. 石油天然气学报,2012,34(3):96-100,167.]
[4] Pu Jie, Wu Xia, Li Hang.Reseach of the solid potash salts logging method in Sichuan Basin[J]. Computer Applications of Petroleum ,2013,(1):31-34.
. 石油工业计算机应用,2013,(1):31-34.]
[5] Chen Kegui,Li Li,Li Chunmei, et al . Exolution of the potash-rich areas in evaporation baisn during the epigenetic stage with continental block being active[J]. Advances in Earth Science ,2014,29(4):515-522.
. 地球科学进展,2014,29(4):515-522.]
[6] Song Yanjie, Zhang Jianfeng, Yan Weilin, et al . A new identification method for complex lithology with support vector machine[J]. Journal of Daqing Petroleum Institute ,2007,31(5):18-20.
. 大庆石油学院学报,2007,31(5):18-20.]
[7] Liang Ya’na. Support Vector Machine in Logging Curve Hierarchical Application[D].Chengdu:Chengdu University of Technology,2012.
.成都:成都理工大学,2012.]
[8] Chen Kegui, Chen Xu, Zhang Jiahao. Combined methods of permeability logging evaluate in glutenite reservoirs—A case study of Badaowan Formation in Karamay Oilfield[J]. Advances in Earth Science ,2015,30(7):773-779.
. 地球科学进展,2015,30(7):773-779.]
[9] Chen Kegui, Li Li, Wang Gang, et al . Analysis of logging response characteristics and potassium-forming conditions of early and middle Triassic Strata in Nanchong Basin[J]. Mineral Deposits ,2014,33(5):1 069-1 080.
. 矿床地质,2014,33(5):1 069-1 080.]
[10] Huang Xi.The Enrichment Regularity of Triassic Potassium-rich Brines of the Salt-bearing Sichuan Basin[D].Beijing:China University of Geosciences(Beijing),2013.
.北京:中国地质大学(北京),2013.]
[11] Huang Xuanzhen.The first polyhalite deposit of China[J]. Yunnan Geology ,1996,15(1):52-61.
. 云南地质,1996,15(1):52-61.]
[12] Liu Dejun, Ran Qunying, Wang Bin.Application of support vector machine in logging analysis of Qijia sagin Daqing Oilfiel[J]. Geophysical Prospecting for Petroleum ,2007, 46(2):156-161.
. 石油物探,2007,46(2):156-161.]
[13] Li Xinhu. Lithology identification methods contrast based on support vector machines at different well logging parameter[J]. Coalgeology & Exploration ,2007,35(3):72-76,80.
. 煤田地质与勘探,2007,35(3):72-76,80.]
[14] Zhang Ying, Pan Baozhi. The application of SVM and FMI to the lithologic identification of volcanic rocks[J]. Geophysical and Geochemical Exploration ,2011,35(5):634-638,642.
. 物探与化探,2011,35(5):634-638,642.]
[15] Yexin Liu. Support Vector Machine (SVM)-based geostatistical inversion: An integrated solution to honor well-log data and seismic attributes[C]∥CSPG CSEG CWLS Convention.Alberta,Canada,2009: 309-312.
[16] Wang Guochang, Carr T R, Ju Yiwen, et al. Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian Basin[J]. Computers & Geosciences , March ,2014, 64: 52-60,doi:10.1016/j.caqeo.2013.12.002.
[17] Hassan S M, Soliman O S, Mahmoud A S. Optimized data input for the support vector machine classifier using ASTER data. Case study: Wadi Atalla area, Eastern Desert, Egypt[J]. Carpathian Journal of Earth and Environmental Sciences ,2015,10(1): 15-26.
[18] Yu Daiguo, Sun Jianmeng,Wang Huanzeng, et al . A new method for logging lithology identification—SVM[J]. Petroleum Geology & Oilfield Development ,2005,24(5):93-95,110.
. 大庆石油地质与开发,2005,24(5):93-95,110.]
[19] Shu Shiyu. SVM Predictive Optimizationalgorithm and Application Based Onensemble Learning[D].Shanghai:Donghua University,2015.
.上海:东华大学,2015.]

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