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Well log analysis is one of the costliest parts of petroleum fields. It has been realized that developing Synthetic well logs can help analyze the reservoir properties in areas where some necessary logs are absent or incomplete, and then reduce costs of companies. During generating synthetic logs, logging time should be used sufficiently for predicting trends and filling some incomplete logs to obtain consistent and high quality throughout the field. This paper presents a new methodology to generate synthetic well logs and detecting logging trends with time using BP neural network including hash function. In the model for multiple wells analysis, not only several loggings from the same well but the formation similarity among wells can be used effectively. It will provide the possibility to study logs for wells that do not have enough logs needed for the analysis. This hash-based method was confirmed effective through experiments on both real-world and synthetic well log data.