Due to the large amount of data information in the reservoir, it is difficult to process or obtain accurate required parameters. There is a large amount of untreated gas in Daniudi gas field, and there are problems such as small wellhead, high temperature and rapid pressure change. Therefore, intelligent identification of reservoir fluid in Daniudi gas field is studied in this paper. Its purpose is to improve the recognition ability by using machine learning algorithm. This paper mainly uses the methods of experiment and comparison, selects 5 groups of data from the samples for comparison, and expounds the application of related algorithm models in reservoir fluids. The experimental data show that the error data of velocity density as input set and sensitive parameter are not very different, mostly within 1. But the input of sensitive parameters can get smaller error. Therefore, parameters with high sensitivity can be added for fluid identification.

Junmin Gull. Intelligent Identification of Reservoir Fluid in Daniudi Gas Field Based on AdaBoost Machine Learning Algorithm. Machine Learning Theory and Practice (2020), Vol. 1, Issue 3: 20-28. https://doi.org/10.38007/ML.2020.010303.

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