Classification and Recognition of Polyhalite in Chuanzhong Based on Support Vector Machine
First author:Chen Kegui(1959-), male, Zigong City, Sichuan Province,Professor.Research areas include petroleum geology, technology of well logging reservoir evaluation and survey research potash in Sichuan.E-mail:chenkegui@21cn.com
*Corresponding author:Wu Liulei(1992-),male, Rugao City,Jiangsu Province, Master student. Research areas include logging interpretation.E-mail:1556883301@qq.com
Received date: 2016-07-18
Revised date: 2016-09-15
Online published: 2016-10-20
Supported by
Project supported by the National Natural Science Foundation of China “The study on geophysical evaluation method of oil and potash in Sichuan Basin” (No.41372103);The State Key Development Program for Basic Research of China “The study on potassium regularity and prediction of marine sediment on the China continental block”(No.2011CB403002)
Copyright
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.
Kegui Chen , Liulei Wu , Yuanyuan Chen , Gang Wang . Classification and Recognition of Polyhalite in Chuanzhong Based on Support Vector Machine[J]. Advances in Earth Science, 2016 , 31(10) : 1041 -1046 . DOI: 10.11867/j.issn.1001-8166.2016.10.1041
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