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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 Liulei1, *, 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        http://www.adearth.ac.cn/CN/Y2016/V31/I10/1041

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