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Currently, the challenges of oil/gas target prospecting by surface soil geochemical exploration are the covers of the prospecting regions. And the arid, sandy, undeveloped deserts of Xinjiang, China, are merely the geographic challenges facing today’s oil and gas exploration and production industry. While there is too often much complex information to assimilate and understand for quick and accurate decisions leading to improved reservoir management, advanced technology, such as the development of newer algorithms and numerical methods that can be helpful in exploration to process large and/or old geochemical datasets to improve targeting of buried oil/gas geochemical systems. Herein, we investigated the possibility that geochemical concentration distribution in space may be members of a special class of complex processes, termed as multifractal, which require a large number of exponents to characterize their scaling properties. In addition, the results present that the 15 petroleum indices of study could be classified into three groups according to their multifractal spectra. The three groups of the indices are discriminated well by several multifractal parameters. Such discrimination results are in agreement well with the analytical results by statistical cluster analysis results and principal component analysis method (PCA) included in the software package of GeoDAS. Based on the PCA results, C-A (concentration-area) fractal method has been applied in this paper to delineate the geochemical anomaly areas for petroleum resources prediction. The first and second components clearly represent the potential petroleum prospecting areas which correspond well to the discovered oil/petroleum sites. Both the multifractal method and spatial analysis technique in this paper provide new insights into the selection of target indices of petroleum geochemical exploration as well as comprehensive information extraction for oil/gas target prospecting by surface soil geochemical exploration.