多相流软测量的增强贝叶斯估计框架
Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing
摘要：
在这项工作中，作者研究了多相流软测量问题的基础上，以前建立的框架。该框架有三个功能模块，即瞬态井流模型，描述井中某些物理变量，例如温度、速度和压力，对进入和离开井区的流量的响应;马尔可夫跳跃过程，旨在捕捉流量的潜在突变;以及用于基于来自安装在井中的物理传感器的测量结果来估计潜在流速的估计方法。在以往的研究中，马尔可夫跳过程中的流量的方差是手动选择的。为了填补这一空白，在目前的工作中，提出了两种自动方法，以优化方差估计。通过一个数值例子，我们表明，当估计框架与这两个提出的方差估计方法结合使用时，它可以实现合理的性能，在匹配物理传感器的测量值和真实的底层流量方面。
英文摘要：
In this work the authors study the multiphase flow soft-sensing problem based on a previously established framework. There are three functional modules in this framework, namely, a transient well flow model that describes the response of certain physical variables in a well, for instance, temperature, velocity and pressure, to the flow rates entering and leaving the well zones; a Markov jump process that is designed to capture the potential abrupt changes in the flow rates; and an estimation method that is adopted to estimate the underlying flow rates based on the measurements from the physical sensors installed in the well. In the previous studies, the variances of the flow rates in the Markov jump process are chosen manually. To fill this gap, in the current work two automatic approaches are proposed in order to optimize the variance estimation. Through a numerical example, we show that, when the estimation framework is used in conjunction with these two proposed variance-estimation approaches, it can achieve reasonable performance in terms of matching both the measurements of the physical sensors and the true underlying flow rates.
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