The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The K-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of K-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accuracy of production layers.

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