## 1 Introduction  
Foreland basins hold over 40% of the world's conventional hydrocarbon resources and are an important field for deep and ultra-deep oil and gas exploration and development [1, 2]. However, complex reservoir formation conditions, such as multi-sources [3, 4, 5], spatiotemporal differences in sources [6, 7, 8, 9], various sedimentary environments [10, 11, 9, 11],heterogeneity of sources and reservoirs [12; 13; 14], and the mixing of multi-stage charged reservoir oils [7; 15; 16], have impeded the prediction of the prospective hydrocarbon accumulation areas. Therefore, clarifying oil-source correlations is crucial to further hydrocarbon exploration and development, especially in structurally active foreland basins. Advanced organic geochemical methods have been used since the late 1970s for oil-source correlation [17; 18]. Source rock pyrolysis parameters, consisting of total organic carbon (TOC), hydrogen index (HI), hydrocarbon generating potential (S\({}_{1}\) + S\({}_{2}\)), and the highest pyrolysis peak temperature (T\({}_{\rm max}\)), were generally used for the identification and evaluation of source rocks [10; 19], providing insight on the hydrocarbon prospectivity in sedimentary basins. The oil-source correlations have been used to determine if there is a genetic relationship between oils and sources by comparing geochemical data between reservoir oils and source rock extracts. [PERSON] et al. (1997) [20] developed online and offline fluid inclusion analytical methods termed the Molecular Composition of Inclusions (MCI) methods to obtain extracts of inclusion oils for geochemical analysis. These methods avoid the loss of light hydrocarbon components, contamination, and secondary alterations of the hydrocarbons in the analysis so as to reflect the geochemical information more accurately. The MCI analysis is less able to distinguish the oil inclusions with different fluorescence colors and charge timing in the same sample [21]. Consequently, it is necessary to select the samples with a single type of oil inclusions for MCI analysis. Some studies have shown that the geochemical properties of inclusion oils may sometimes be quite different from those of the associated reservoir oils [22; 23; 24]. These differences may reflect changes in reservoir oils over time [25], may be the result of differences in sources [26; 27; 28], or may be the result of fractionation in reservoirs [22; 29]. In recent years, statistical methods, such as multivariate analysis, have developed rapidly and have been widely applied in the field of organic geochemistry [30; 31; 32]. Multivariate analysis can be used to discriminate oils from different types of sources and depositional environments, as well as from different levels of thermal maturity [33; 34; 35]. The multivariate statistical approach includes hierarchical clustering analysis (HCA) and principal component analysis (PCA). The combination of conventional organic geochemical analyses and interpretations with statistical analysis provides the most reliable oil-oil and oil-source correlations. Basin and petroleum system modeling (BPSM) is a useful tool for the analysis of hydrocarbon generation, migration, and accumulation, for visualizing hydrocarbon migration pathways and calculating hydrocarbon yields in geological periods [36; 37; 38; 39]. This can provide guidance for delineating areas favorable for hydrocarbon accumulation [40; 41; 42]. Through 2D basin modeling, the hydrocarbon generation from each set of source rocks during various geological periods can be quantitatively simulated to evaluate and predict the possible sources of oil inclusion extracts (paleo) and reservoir oils (present). Recent progress has been achieved in the exploration of deep oil and gas resources within the Kuqa Foreland Basin, Tarim Basin, NW China [43].