To identify conveniently multiphase flow regimes in subsea pipeline-risers, we study in this paper experimentally two-phase flows in a 1657 m long pipeline with an S-shaped riser to simulate field experiment, within a wide range of gas and liquid velocities. Three flow regimes, namely severe slugging, transitional flows, and stable flows, are analyzed based on three differential pressure and one pressure signals at the top of the riser; comparatively speaking, the positions of these signals in the experimental system are similar to those of the sea level signals in industrial fields, which are easy and less expensive to obtain. The obtained signals are decomposed into six scales via a multi-scale wavelet analysis, and further four statistical parameters on each scale are extracted, including mean values, standard deviations, ranges, and mean values of absolute. We compared the effects of six SVM classifiers with different kernel functions on the recognition rate of flow regimes, and it is found the recognition rates of SVM classifier with quadratic and cubic kernel functions are the highest. Further, the principal component analysis is employed to reduce the dimension of statistical parameters and it indicates that the recognition rate tends to increase with the rising number of principal components from 1 to 6, and it remains constant if the principal component number is further increased. Moreover, The results suggest that the recognition rate obtained from the pressure difference between the top of the riser and the separator peaks, and then it comes that from the pressure signal at the top of the riser, and that for the pressure difference signal at the top of the riser is the least satisfying one. As for the optimal differential pressure signals between the top of the riser and the separator, the results show that the recognition rate increases rapidly from 70.2% to 90.4% when the sample duration rising from 2.3 s to 18.6 s, and when the sample duration exceeds 74.4 s, the recognition rate exceeds 92.9% and remains unchanged.

为了方便地识别海底管道立管中的多相流态，我们在本文中通过实验研究了 1657 m 长的带有 S 形立管的管道中的两相流，以模拟现场实验，在很宽的气体和液体速度范围内。基于立管顶部的三个压差和一个压力信号，分析了严重段塞流、过渡流和稳定流三种流态；相比较而言，这些信号在实验系统中的位置与工业领域海平面信号的位置相似，获取容易且成本较低。通过多尺度小波分析将获得的信号分解为六个尺度，并进一步提取每个尺度上的四个统计参数，包括平均值、标准差、范围、和绝对值的平均值。我们比较了六种不同核函数的SVM分类器对流态识别率的影响，发现二次核函数和三次核函数的SVM分类器识别率最高。进一步采用主成分分析来降低统计参数的维数，表明随着主成分数从1增加到6，识别率有增加的趋势，如果进一步增加主成分数，识别率保持不变。此外，结果表明，从立管顶部和分离器之间的压力差获得的识别率达到峰值，然后来自立管顶部的压力信号，而立管顶部的压差信号是最不令人满意的。对于提升管顶部与分离器之间的最佳压差信号，结果表明，当采样持续时间从2.3 s上升到18.6 s时，识别率从70.2%迅速提高到90.4%，当采样持续时间超过74.4 s，识别率超过92.9%，保持不变。