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We imaged water-wet and oil-wet sandstones under two-phase flow conditions for different flooding states by means of X-ray computed microtomography ( CT) with a spatial resolution of 2.1 m/pixel. We systematically study pore-scale trapping of the nonwetting phase as well as size and distribution of its connected clusters and disconnected globules. We found a lower or , 19.8%, for the oil-wet plug than for water-wet plug (25.2%). Approximate power-law distributions of the water and oil cluster sizes were observed in the pore space. Besides, the value of the wetting phase gradually decreased and the nonwetting phase gradually increased during the core-flood experiment. The remaining oil has been divided into five categories; we explored the pore fluid occupancies and studied size and distribution of the five types of trapped oil clusters during different drainage stage. The result shows that only the relative volume of the clustered oil is reduced, and the other four types of remaining oil all increased. Pore structure, wettability, and its connectivity have a significant effect on the trapped oil distribution. In the water sandstone, the trapped oil tends to occupy the center of the larger pores during the water imbibition process, leading to a stable specific surface area and a gradually decreasing oil capillary pressure. Meanwhile, in oil-wet sandstone, the trapped oil blobs that tend to occupy the pores corner and attach to the walls of the pores have a large specific surface area, and the change of the oil capillary pressure was not obvious. These results have revealed the well-known complexity of multiphase flow in rocks and preliminarily show the pore-level displacement physics of the process.

The formation mechanism and utilization conditions of the remaining oil in the high water cut period play significant roles in improved tapping potential and enhanced oil recovery. The classification of the remaining oil is a difficult point, meanwhile a burning issue. However, the current classification method is mainly through the manual method to determine the boundaries of classification, time-consuming and has a great subjectivity. Machine learning and data mining methods in recent years have been widely used in the field of petroleum engineering, such as prediction of the recovery factor and so on, especially the well-known k-means classification algorithm. The first objective of this paper is to use the semi-supervised learning (SSL) method to realize the classification of remaining oil in the high water cut period, based on the database obtained from experiments of 2D etched glass micro-model, with the help of the technique of quantitative characterization of pore structure and micro-residual oil. The method of principal component analysis (PCA) is used to reduce the dimension of the data. According to the formation causes, remaining oil can be divided into four types: oil film, throat retained oil, heterogeneous multi-pores oil and clustered oil. Two typical blocks are identified manually for each class, with an increased weight coefficients, then the other oil blocks with smaller weights are clustered into their types by the seeded k-means algorithm. The result shows that semi-supervised method is more effective than both supervised learning (with manual boundaries) and unsupervised learning methods. Based on the classification, the effects on the formation of heterogeneous multi-pores oil and throat retained oil are analyzed by statistical method. All of these quantitative studies can provide theoretical guidance for the use of residual oil in high water cut periods and increased oil recovery.

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