Abstract Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.

中文翻译：

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使用改进的 ORB 和 RANSAC 算法测量低速高含水油水两相流的油相速度

摘要 速度是剖面测井流体流动特性的重要参数。粒子追踪测速（PTV）技术常用于研究低流速高含水油井的流动特性，而PTV技术的关键是粒子匹配。现有的PTV技术粒子匹配算法不能满足低流速高含水油水两相流油相流速测量中油滴的匹配需求。为了提高粒子匹配精度，我们改进了面向FAST和旋转BRIEF（ORB）特征描述和随机样本一致性（RANSAC）算法的粒子匹配算法。进行了模拟和实验。仿真结果表明，改进后的算法不仅增加了匹配点数，而且减少了计算量。实验表明，本文改进算法不仅减少了特征描述过程的计算量，达到原算法计算量的一半，而且增加了匹配结果的数量，从而提高了油相速度的测量精度。 . 与SIFT算法和ORB算法相比，改进算法的匹配点对数最多。并且该算法的变异系数为0.039，表明该算法是稳定的。改进算法油相速度测量的平均误差为1.20%，最大误差为6.16%，远低于PTV的最大误差，为 25.89%。改进后的算法克服了SIFT算法计算成本高的问题，达到了SIFT算法的精度。因此，本研究有助于提高油相速度测量精度，为油田提供可靠的生产测井资料。