Asphaltenes have always been an attractive subject for researchers. However, the application of this fraction of the geochemical field has only been studied in a limited way. In other words, despite many studies on asphaltene structure, the application of asphaltene structures in organic geochemistry has not so far been assessed. Oil-oil correlation is a well-known concept in geochemical studies and plays a vital role in basin modeling and the reconstruction of the burial history of basin sediments, as well as accurate characterization of the relevant petroleum system. This study aims to propose the X-ray diffraction (XRD) technique as a novel method for oil-oil correlation and investigate its reliability and accuracy for different crude oils. To this end, 13 crude oil samples from the Iranian sector of the Persian Gulf region, which had previously been correlated by traditional geochemical tools such as biomarker ratios and isotope values, in four distinct genetic groups, were selected and their asphaltene fractions analyzed by two prevalent methods of XRD and Fourier-transform infrared spectroscopy (FTIR). For oil-oil correlation assessment, various cross-plots, as well as principal component analysis (PCA), were conducted, based on the structural parameters of the studied asphaltenes. The results indicate that asphaltene structural parameters can also be used for oil-oil correlation purposes, their results being completely in accord with the previous classifications. The average values of distance between saturated portions (dr) and the distance between two aromatic layers (dm) of asphaltene molecules belonging to the studied oil samples are 4.69? and 3.54?, respectively. Furthermore, the average diameter of the aromatic sheets (La), the height of the clusters (Lc), the number of carbons per aromatic unit (Cau), the number of aromatic rings per layer (Ra), the number of sheets in the cluster (Me) and aromaticity (fa) values of these asphaltene samples are 10.09?, 34.04?, 17.42?, 3.78?, 10.61? and 0.26?, respectively. The results of XRD parameters indicate that plots of dr vs. dm, dr vs. Me, dr vs. fa, dm vs. Lc, Lc vs. La, and fa vs. La perform appropriately for distinguishing genetic groups. A comparison between XRD and FTIR results indicated that the XRD method is more accurate for this purpose. In addition, decision tree classification, one of the most efficacious approaches of machine learning, was employed for the geochemical groups of this study for the first time. This tree, which was constructed using XRD data, can distinguish genetic groups accurately and can also determine the characteristics of each geochemical group. In conclusion, the obtaining of structural parameters for asphaltene by the XRD technique is a novel, precise and inexpensive method, which can be deployed as a new approach for oil-oil correlation goals. The findings of this study can help in the prompt determination of genetic groups as a screening method and can also be useful for assessing oil samples affected by secondary processes.