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Xihu Depression is a relatively new petroleum exploration area in East China Sea Basin. Pinghu Formation is one of the most important oil-bearing formations in the depression. Due to the low exploration level of the depression, paleosalinity and deposition environment of the depression in Pinghu stage is not clear. Element geochemical analysis of samples from different structural units is used to study ancient water salinity and reveal the depositional environment. B/Ga ratio, which is sensitive to paleosalinity in study area, is 0.9–4.0 and indicates characteristics of mixed water of fresh and salt water. There is a threshold for Sr/Ba in water salinity study in the study area. When Sr/Ba ratio is lower than 0.15, it cannot be used as an indicator for salinity and ancient water body type. Equivalent boron content calculated by Walker's method is lower than 150 ppm. Paleo salinity from boron content calculated by Couch's method is 2.97‰–9.61‰ and the average is 4.8‰. Based on the above mentioned results, ancient water body of Xihu Depression in Pinghu stage has typical characteristics of brackish water and it belongs to oligohaline and mesohaline water. Combining paleogeomorphology and sedimentary facies distribution in the study area, paleowater body in Xihu Depression during Pinghu stage is transitional mixed water influenced by terrigenous diluted water. Paleosalinity contour lines extend on plane from north to the south and are parallel to paleo-coastline. Paleosalinity increases from the west to the east. Influenced by freshwater of deltas at western part of the depression, high abnormal salinity values occur around the wells M1 and L1 where salinity contour lines curve westward.

It is difficult to extract quantitative information of pore space from thin section. A novel method to characterize the pore space using multifractal analyses is presented, with the help of mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) transversal relaxation time ( T 2 ) data. Thin sections were processed via multi-threshold segmentation using particle swarm optimization (PSO) algorithm to obtain binary images containing pore space and mineral matrix. After multifractal analyses of binary images, the interrelationship between multifractal parameters and pore structure is investigated in detail. A crossplot for pore structure classification is put forward with types predefined by MICP and NMR experiments. The result shows that different pore types demonstrate their unique multifractal characteristics. Multifractal dimensions are positively correlated with the median capillary pressure, whereas negatively correlated with average pore radius and the T 2 geometric value. D min and D 0 can be served as good indicators of pore structure types in the case study. This study provides an effective way of pore structure characterization and classification based on thin section, especially for regions without MICP and NMR experiments.