Pore structure classification and logging evaluation method for carbonate reservoirs: A case study from an oilfield in the Middle East

Abstract

ABSTRACTBased on the data of 121 thin sections, 24 mercury injection and physical properties of the carbonate reservoir in A oilfield of the Middle East, the reservoir in the study area is divided into four pore-throat systems by analyzing the pore-throat volume verses permeability-contribution curve of core mercury injection and corresponding depth NMR logging data. Taking into account the contribution of each pore-throat system to the rock, a new pore structure parameter P based on NMR logging data is proposed. On this basis, the P and flow porosity calculated from NMR logging data are used as variables, and the pore structure of carbonate reservoirs is divided into four types by using the K-means clustering method in combination with the characteristics of capillary pressure curves and thin sections. With the input of NMR logging data and conventional logging data, the classification model of pore structure is established by Rotation Forest algorithm. The accuracy of the classification model based on NMR logging is 98.56%, and the accuracy of the classification model based on conventional logging is 89.9%. Compared with the Random Forest algorithm and the Fisher discriminant method, the Rotation Forest algorithm has high prediction accuracy and strong stability. The research shows that the pore structure classification method proposed in this paper is in good agreement with the interpretation results, which can provide some reference value for finding effective reservoirs in the future.