| Citation: | Li Yang, Dai Zongyang, Zhang Jiewei, Xiao Duoyan, Li Dan, Zhao Xiaoyang, Li Tian, Huang Lan, Huang Youlin. Multiparameter reservoir evaluation method based on unsupervised learning: A case study of the reef beach reservoir of the Lower Triassic Feixianguan Formation in the Pubaoshan area[J]. Bulletin of Geological Science and Technology, 2023, 42(5): 285-292. doi: 10.19509/j.cnki.dzkq.2022.0154
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The formation and development of the reef-shoal reservoirs in the Lower Triassic Feixianguan Formation in the Pobaoshan area are the result of the comprehensive action of the geological historical period. Therefore, only using a single factor in reservoir evaluation will inevitably lead to deviations.
The k-means cluster analysis method and principal component analysis method were used to classify and evaluate the reservoir in the study area.
The results show that: On the premise that three different influencing factors of dolomite thickness, average porosity and effective reservoir thickness of the Lower Triassic Feixianguan Formation reef-shoal reservoir in the Pubaoshan area are known, gridding different planes to extract reservoir characteristic data of different influencing factors. The combined elbow method and contour method are used to analyze reservoir characteristic data and divide the reservoir into 4 development types. Then k-means cluster analysis method is applied to assign class attributes to the known data points. Using principal component analysis to reduce the dimensionality of different reservoir characteristic data to form a new comprehensive parameter. The parameter contribution rate can reach 0.882. According to the classification results of k-means, the mean values of the comprehensive parameters of different types of principal component analysis of the four reservoirs were calculated, which were 0.404, 0.640 and 0.716, respectively, as the demarcation point of the reservoir evaluation zone. Finally, this quantitative method is used to reasonably integrate the different characteristic plans of the study area to form a comprehensive evaluation map of the reservoir.
The research results can effectively classify and evaluate the reservoir in the study area and predict favorable exploration areas.
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