Experimental Data-Analysis Methods Analyze Multiphase-Flow Data Sets

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32170, “Performance Mapping of Multiphase-Flow Model—Connecting the Dots From Experimental Data,” by Auzan A. Soedarmo, SPE, Eduardo Pereyra, SPE, and Cem Sarica, SPE, The University of Tulsa. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ 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. Therefore, it is easy to lose sight of the larger context in terms of current understanding and modeling capability. This disconnected approach also has produced an ever-growing, potentially unmanageable list of closure relationships, which can be counterproductive for model development. Experimental Data-Analysis Methodology For their study, the authors collected near-horizontal (–5° to +5°) gas/liquid data from literature with a gas density of 5 kg/m3 or greater. Atmospheric data were excluded because gas and air at such conditions are typically very compressible, meaning that superficial gas velocity can vary considerably along the pipe. One also can argue that high-gas-density cases are more relevant to typical high-pressure field applications. The authors collected more than 2,000 data points for pressure drop, holdup, and flow patterns. This accumulated data set by no means can be categorized as “big data” in a traditional sense; however, the authors write that it is sufficient for demonstrating how exploratory data analysis may extract actionable insights from data sets from various independent sources. This data set is henceforth called the “HP data set.” No high-viscosity liquid (10 cP or higher) is included in the HP data set. When comparing experimental data collected under different conditions, it is helpful to reduce the problem’s dimensionality (down from 10 dimensions in two-phase gas/liquid flow, excluding the gravitational acceleration constant). A traditional approach would be to use dimensionless numbers such as Reynolds and Froude numbers. A single dimensionless number, however, can only account for limited parameters. For example, the Reynolds number only considers the inertia and viscous effects, while the Froude number only considers the inertia and gravity effects. Therefore, a mechanistic multiphase-flow model is used in this study that accounts for all dimensions of the multiphase-flow problems and reduces them into one dimension, namely the discrepancy between the predicted value and experimental data.