Abstract

The prediction of liquid holdup and multiphase flow regimes present in a well or pipeline is very important to the petroleum industry. Liquid holdup, defined as the fraction of pipe occupied by liquid, and flow regimes must be predicted to design separation equipment and slug catchers in pipeline operations properly. It is also important when designing gas storage fields in depleted oil reservoirs.

A new methodology was developed to model multiphase flow conditions for pipelines and wellbores using only known surface data, This methodology, which has been named Virtual Measurement in Pipes (VMP), incorporates an innovative use of information technology and computational intelligence, to address the development of tools for the engineer to use in the design process for a variety of conditions. Artificial neural networks (ANN) were used to develop a Virtual Measurement Tool to survey the liquid holdup and flow regimes in nonspecific multiphase flow systems using readily available data.

The VMP methodology was tested for validity by comparing virtually measured values with published measurements, As a result, the method proved to be an accurate virtual measuring tool to predict liquid holdup and flow regimes in multiphase flowing pipelines and wellbores, The VMP methodology also demonstrated an enhancement to existing industry recognized correlations.

Introduction

Flow of gas and liquid occurs frequently in pipelines and wellbores where the accurate calculation of a pressure drop is of considerable interest to the petroleum industry. Similar conditions exist in the chemical and nuclear industries where two-phase mixtures coexist. In the petroleum sector, gas-liquid mixtures are transported over long distances in a common line under large pressure drops which influence the design of the system. Other important areas of application can be cited as gas lift operations and wellhead gathering systems. Practically all oilwell production design involves evaluation of flow lines under two-phase flow conditions, However, the uncertainties in flow regime determination greatly affect the pressure drop predictions. A method is desired for accurate calculation of pressure losses.

Pressure losses in two-phase, gas-liquid flow are different from single-phase flow, An interface exits in most cases and gas slips past the liquid with a surface of varying degrees of roughness depending on the flow pattern. Each phase flows through a smaller area than if it flows alone causing high pressure losses when compared to single-phase flow, Additionally, this segregated flow changes at any point along the flow path during the fluctuating flows. Under the conditions of distributed phases, prediction of fluid mixture properties like density and viscosity becomes a challenge for the design engineer.

The density and viscosity along with the velocity are important terms in the determination of pressure losses in any pipe system. Several correlations are proposed to define the holdup and flow patterns for horizontal, vertical, and inclined pipes. In general, these correlations are based on experimental work conducted under specific conditions such as a constant pipe diameter.

The application of artificial neural networks in the petroleum industry is recent and its potential is not fully investigated. This technology is applicable in many areas where an existing pattern is not obvious to the naked eye of the researcher as is the case of log evaluations. Complex patterns and relationships in data, such as holdup and flow pattern, can be established through an artificial neural network.

Approach

A new methodology is introduced to investigate the holdup and flow pattern determination problem in pipes under multiphase conditions. This approach uses the measured data to determine the relationship between input and output parameters, The Virtual Measurement in Pipes (VMP) tool utilizes the pattern recognition capabilities of an artificial neural network.

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