Flow assurance

Experimental Data-Analysis Methods Analyze Multiphase-Flow Data Sets

In this paper, the authors present data analyses to comprehensively evaluate the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments.

2-D discrepancy plot of TUFFP Unified model pressure drop predictions on HP dataset
2-D discrepancy plot of TUFFP Unified model pressure drop predictions on HP dataset, which Lockhart-Martinelli parameters as the axes and relative errors as the error parameter.

In the complete paper, the authors present exploratory data analyses to evaluate comprehensively the performance of a steady-state multiphase-flow point model in predicting high‑pressure, near-horizontal data from independent experiments. This effort provides wide-ranging insight that can reflect the current state of the art of multiphase-flow modeling and pinpoint areas where improvements are needed. Much of the complete paper is devoted to the literature relating to the authors’ work; these specific citations are not included in this synopsis.

Introduction

The emergence of “big data” has encouraged the use of data from various sources to enhance the decision-making process. The authors write that, unfortunately, multiphase-flow studies often are performed in theoretical silos, within which specific experiments were performed and upon which certain model improvements were proposed.

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