Most oilfields in China have entered the late stage of development，and the conventional oil and gas reserves are gradually exhausted. Therefore，unconventional oil and gas reservoirs such as low-resistance oil layers have become important targets of exploration. In complicated fault-block reservoirs，affected by multiple factors such as sedimentary microfacies，structure，and interlayer interference，manual identification is inaccurate and inefficient，simply relying on expert experience. In this regard，big data mining technology was adopted. Firstly，the low-resistance oil layers were screened and verified with sub-layer data as a pointcut through the combination of logging data and research results；then the relationships between oil-bearing-related parameters of sub-layers were analyzed by the parallel association rule algorithm；finally，all sub-layers were classified by clustering analysis algorithm，and the similarity on the sub-layers containing verified low-resistance oil layers were calculated. As a consequence，the low-resistance oil layers were identified. The analysis of substantial data from an oil field in the eastern region shows that the accurate identification method of low-resistance oil layers driven by big data can tap the potential of low-resistance oil layers，with an accuracy rate of 90%. The potential reservoirs selected in the oilfield were put into production，with a great oil increment. This method saved massive manpower，reduced development costs，and enhanced oil recovery.