A Big Data Mining in Petroleum Exploration and Development
We take a well log in petroleum exploration and development as an example of the big data mining, and adopt three regression and two classification algorithms: the multiple regression analysis (MRA), the error back-propagation neural network (BPNN), the regression of support vector machine (R-SVM), the classification of support vector machine (C-SVM), and the Bayesian successive discrimination (BAYSD). It is well known that MRA, BPNN and R-SVM are regression algorithms while C-SVM and BAYSD are classification algorithms, and only MRA is linear algorithm whereas the other four algorithms are nonlinear algorithms. From this case study, we can draw the following five major conclusions: a) Since C-SVM is the best classifier, it is employed as a data cleaning tool. b) Since MRA is a linear algorithm, its total mean absolute relative residual R*(%) can express the nonlinearity of studied problem. For this case study, R*(%)=52.14 showing the nonlinearity of the studied problem is strong. c) Since both MRA and BAYD can establish the order of dependence between a dependent variable and independent variables, each of MRA and BAYD could serve as a pioneering dimension-reduction tool in data mining. In the case study, since the nonlinearity of the studied problem is strong, the nonlinear algorithm BAYSD can serve as a pioneering dimension-reduction tool, but the linear algorithm MRA cannot. d) Since the nonlinearity of the case study is strong, BPNN and R-SVM are not applicable though they are nonlinear algorithms, whereas other two nonlinear algorithms C-SVM and BAYSD are applicable, indicating the nonlinear ability of C-SVM and BAYSD is higher than that of BPNN and R-SVM. e) Comparing the two applicable algorithms C-SVM and BAYSD for this case study, it is seen that R*(%) of C-SVM is less than that of BAYSD; BAYSD can serve as a pioneering dimension-reduction tool, but C-SVM cannot; it is easy to code the BAYSD program whereas it is very complicated to code the C-SVM program, so BAYSD is a good software for this case study when C-SVM is not available.Key words: Big data mining; Well log; Data cleaning; Dimension-reduction; Regression; Classification
 Le Maitre, R. W. (1984). A proposal by the IUGS subcommission on the systematics of igneous rocks for a chemical classification of volcanic rocks based on total alkali silica (TAS) diagram. Australian Journal of Earth Science, 31(2), 243-255.
 Qiu, J. X. (1991). Brief introduction of a classification of volcanic rocks recommendations of the IUGS subcommission on the systematics of igneous rocks. geoscience, 5(4), 457-468.
 Zhu, Y. X., & Shi, G. R. (2013). Identification of lithologic characteristics of volcanic rocks by support vector machine. Acta Petrolei Sinica, 34(2), 312-322.
 Shi, G. R. (2013). Data mining and knowledge discovery for geoscientists. USA: Elsevier Inc.
 Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines (Version 3.1) [Online forum comment]. Retrived from http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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