Oil-Water Flowing Experiments and Water-Cut Range Classification Approach Using Distributed Acoustic Sensing

The accurate measurement of dynamic water cut is of great interest for analyzing reservoir performance and optimizing oilwell production. Downhole water-cut measurement is a very challenging work. Moreover, the surface-measured water cut is a comprehensive indicator of commingled producing well and it is difficult to use this parameter to deduce the downhole water cut of each contributing layer. In this paper, we propose to use distributed fiber-optic acoustic sensing (DAS) technology for the classification of water-cut range. DAS can dynamically monitor the entire wellbore by “listening” to the acoustic signals during flow. A large number of laboratory experimental data from DAS have been collected and analyzed using wavelet time scattering transform and short-time Fourier transform (STFT). The extracted low-variance scattering feature, short time-frequency feature, and fusion feature (combination of two extracted features) were learned with backpropagation (BP) neural network, decision tree (DT), and random forest (RF) algorithm. Then, a classification method of water-cut range in oil-water flow was established with machine learning. Field DAS data were collected from two oil wells to verify the effectiveness of the proposed method. The classification accuracies for the vertical well (Well A) are 92.4% and 87.4% by DT and RF model, respectively. For the horizontal well (Well B), the average classification accuracy exceeds 90% for all three methods. Water shutoff measure was conducted in Well B, and an obvious water decrease was realized. The result shows that the fusion feature overweighs single feature in machine learning with DAS data. This study provides a novel way to identify downhole water-cut range and detect water entry location in horizontal, vertical, and deviated oil-producing wells.