Lithofacies identification model based on fine Gaussian support vector machine——Take block B as an example
B well area long eight reservoir for the region's main resource potential layer system,through its logging data can be found that the region is mainly mudstone,fine sandstone,its main reservoir type is fine sandstone,the fine engraving of the single sand body for the later study of the sedimentary microphase characteristics,the single sand body characteristics as well as the subsequent exploration and development work is of some significance to the actual production of this block in the production of wells with a large number of characteristics such as large workload,so the efficient and accurate implementation of the single sand body engraving is an urgent problem of oil field production test oil selection,In the actual production process of this block,there are many production wells and a large workload,so the efficient and accurate single sand body engraving is an urgent problem to be solved in the oilfield production test oil layer selection.The traditional method of single sand body en-graving only needs two curves of natural gamma and natural potential to identify the single sand body of the target layer by jud-ging its curve morphology,which is low in division efficiency and general in accuracy,and its identification effect needs to be further improved from efficiency to accuracy.Therefore,this paper proposes a lithofacies identification method based on a sup-port vector machine model,which is based on the experience of the previous work combined with the actual geological situation of the study area to select the appropriate feature vector as the input layer of its support vector machine model to predict the lithofacies.Based on the previous experience and the actual geological situation of the study area,we select the appropriate feature vectors as the input layer of the support vector machine model to predict the rock phases,and use primary support vec-tor machine,secondary support vector machine for modelling,and finally use Gaussian kernel as the kernel function of the fine Gaussian support vector machine to optimize the model.The method distinguishes itself from traditional methods in terms of the number of data samples as well as the need for feature dimensionality,and the classification samples are not limited to non-linear identification.Thus,the single sand body engraving is efficiently completed,which effectively solves the problem of low efficiency of sand body identification in the study area,and has certain practical significance for the later development of reser-voir characterization research.
support vector machinepetrographic divisionlogging interpretation
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NSTL
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