The fault-controlled fractured-vuggy carbonate reservoirs in the Tarim Basin exhibitconsiderable burial depths, complex structures, and highly developed faults. Faults serve asa dominant factor controlling oil and gas accumulation and possible hydrocarbon migration pathways in the study area. Hence, it is critical to predict their spatial distributions and sizes. There existvariousfault detection attributes, which characterize fault scales and features differently due totheir different calculation methods.Moreover, conventional attribute detection ignores the use and constraints of logs. For more complete and accurate fault prediction results, this study selected multiple fault detection attributes for fusion using the feedforward neural network algorithm, with logs as prior information. First of all, a sample database for fault feature identification was established using fault attributes (like AFE, likelihood, and dip angle) with distinct characteristics anddiscrimination criteria of fault types, including lost circulation data, imaging logs, and seismic event dislocations.The deep feedforward neural network was trained based on the sample database.A neural network prediction model with a minimum prediction error was obtained by comparing and testing the learning effects under different hidden layer depths. Finally, the neural network prediction model was applied to the fault prediction of the study area. The comparative analysis reveals thatthe fault prediction using deeplearning-based fused attributesyielded prediction results more consistent with the log-based interpretation results, and could synthesize the information of faults with different scale characteristics, thus effectively improving the prediction accuracy and reliability.