Identification of logging facies of low-permeability sandstone reservoir based on automatic clustering algorithm

Abstract

The low permeability sandstone reservoir has the characteristics of low petrophysical property and strong interbedded heterogeneity. The classification criteria of reservoir are many and different according to the region. Lithology identification, reservoir classification based on pore structure and physical property characteristics and logging phase classification are commonly used in low permeability reservoir classification. The logging phase can comprehensively characterize the logging response characteristics of the reservoir, and this classification method has great advantages. Well logging data have the advantage of well logging and complete wellbore petrophysical characteristics data and have strong continuity and comparability. According to the well logging data and petrophysical properties of formation, automatic clustering algorithm can be used to divide the logging phase. In this paper, the low-permeability sandstone reservoir in Y member of M oilfield is studied, and it is found that the four relationships of the reservoir in this member are very different, and the diagenetic characteristics and petrophysical characteristics are not clear. Conventional logging data are used to divide logging facies based on automatic clustering algorithm, and then supervised logging facies combination is carried out based on the principle of combining similar logging response characteristics. Finally, logging phases of geological significance can be divided. The results show that the method is well consistent with the actual results in logging phase division and sweet spot identification by training prediction in the whole area, which is of great significance for oilfield exploration and development.