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Gas-Liquid Two-Phase Flow in Pipes

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Gas-liquid two-phase flow is encountered in many industrial processes and is a critical aspect of enormous applications. Examples are oil and gas production, chemical processing, nuclear power plants, aerospace technologies, and renewable energy applications. In the petroleum industry, two-phase flow is a common occurrence in wellbores and pipelines. These pipelines might extend for thousands of miles and through different environments where both flow pressure and temperature change considerably, especially for offshore pipelines. The pipes and wellbores are of varying inclination angles and diameters and they divert liquid and gas through many piping components. As means of connection between the petroleum formation and the processing facilities or export terminals, gas-liquid two-phase flow occurs along these pipes. Design methods are needed to determine the pressure drop, liquid holdup, slug characteristics, and other two-phase flow characteristics that are crucial for designing pipes, pipe components, production equipment, separators, slug catcher, and other processing facilities components for both steady-state and transient operations. Moreover, overcoming flow assurance challenges involves the proper prediction of gas-liquid two-phase flow behavior.

Brief History

Brill and Arirachakaran (1992) summarized two-phase flow modeling history into three distinct eras. The first era represents the starting of wide dependency on two-phase flow modeling in the oil and gas industry. The third era, which extends to this day, involves many advancements that were not foreseen by Brill and Arirachakaran (1992) and it shapes the future of two-phase flow applications in the petroleum industry. The history of multiphase flow models is summarized as follows,

1. The Empirical Period (1950 - 1975)

During this period, most models relied directly on the experimental behavior of the flow and were based on data collected in the field or laboratory flow loops using conventional instrumentation that are of relatively low accuracy. Many models treated gas and liquid as homogeneous mixtures. However, slippage caused by liquid and gas was captured with these models and the liquid holdup is represented with empirical expressions. At that period of time, the instruments used to capture liquid holdup experimentally in fluid flow loops contributed directly to the accuracy of the developed models. Some of the simplest pressure gradient models that are widely used in the oil and gas industry today were developed in this era, for example, Duns and Ros (1963) and Hagedorn and Brown (1965) for vertical flows and Beggs and Brill (1973) for horizontal and near-horizontal flows. A revolution in modeling efforts was sparked in this era with the Drift Flux Model of Zuber and Findlay (1965) which continued to evolve and improve through the rest of the 20th century and until today. In addition to its importance for the petroleum industry, the contribution of Zuber and Findlay (1965) represented a foundation for the accelerated development of many nuclear planet safety flow codes. This period also witnessed extensive classification and observation for flow patterns, namely, the topological distribution of gas and liquid in pipes. This classification resulted in many versions of flow pattern maps such as Baker (1954), Ros (1961), and Beggs and Brill (1973) which were often based on dimensionless numbers.

2. The Awakening Years (1970 - 1985)

During these years, research focused on addressing the lack of physics in the empirical models. It was quickly realized that the available flow pattern maps are inadequate. Similarly, The empirical relationships of liquid holdups resulted in significant prediction errors. Therefore, the 1970s showed a rising adoption of the physical mechanisms of two-phase flow already in use in other industries, e.g. nuclear industries. The classic works of Dukler and Hubbard (1975) and Taitel and Dukler (1976) introduced mechanistic models that consider more of the governing physics for slug flow and flow pattern. The available pressure gradient correlations coupled with the introduction of the personal computer (PC) in the early 1980s made several practical tools available for petroleum engineers to simulate the flow behavior. Simulating the entire production system and integrating the reservoir performance became possible through simple performance relationships. The true concept of NODALTM analyses was born (Brown 1980).

3. The Modeling Era (1980 - Present)

During this era, extensive advancements in instrumentation and data acquisition systems coupled with high-speed PCs allowed high-quality data which enabled testing and improving two-phase flow theory. Steady-state flow models have seen significant improvements with the flow pattern transition criteria introduced by Taitel et al. (1980), Barnea et al. (1982a, 1982b, 1985), and Barnea (1986, 1987). This resulted in the development of mechanistic models that conserve the physics of each flow pattern individually and ensure seamless flow of the calculations at different flow conditions and inclination angles, namely, the "comprehensive" models such as Ozon et al. (1987), Hasan and Kabir (1988), Xiao et al. (1990), Ansari et al. (1994), and Chokshi (1994). These efforts also led to the steady-state "unified" model of Zhang et al. (2003), which eliminates discontinuities of flow patterns at different conditions. Meanwhile, the two-phase models pioneered in the nuclear industry for transient flow application were expanded and integrated into the petroleum industry problems through efforts such as Taitel et al. (1980), Black et al. (1990), and Pauchon et al. (1993). The 1980s witnessed the release of the extensive and well-financed experimental efforts that led to the development of transient flow codes (Bendiksen, 1991). These transient flow models focus on the simultaneous solution of mass and momentum conservation equation for several control volumes changes with flow patterns. These efforts revolutionized offshore petroleum production and led to safe and maintained production and processing through reliable prediction of pipeline pigging, startup/shutdown operation, slugging, and other transient production and flow assurance phenomena. Within the last few years, Artificial Intelligence and Machine Learning applications are being imported and incorporated to improve the empirical parameters within the existing steady-state and transient models. These efforts are accompanied by the advancement of measurement techniques that improved the quality of two-phase flow field data. The utilization of cloud computing is assisting in pushing these techniques forward into the future.


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