To solve the problems of storing hardware and volume of database limitation in the archiving and storing scenario of massive well logging data, a lossless data compression method based on deep learning is proposed in this paper. Data stream is compressed by adaptive arithmetic encoder combining current byte with outputted conditional probability distribution of data stream by recurrent neural network RNN as probability predictor. Data stream is decompressed by saved weights of RNN and arithmetic decoder. Compared with traditional lossless compression methods, compression ratio of one dimensional log data is improved by about 23% averagely and that of two dimensional array log data is improved by about 21% averagely in actual compression test. At the same time, A large scale well logging data storing database constructing method based on multi dimensional featuring indexes querying tree structure is proposed combined with lossless compression method in this paper whose querying efficiency is improved by about 45% compared with traditional database querying method under the multi conditions combining query. Results show that storing space of logging data is decreased effectively with lower data querying time by the method in this paper which provides technical base for storing and utilization of large volume logging data and saves hardware cost and labor cost of data archiving.