Distributed Acoustic Sensing (DAS) technology has been used in an increasing number of applications in the petroleum industry.Multiphase flow rate measurement is an important DAS application. In this work we provide a forward model relating multiphase flow rates and two-phase flow patterns in the wellbore to the DAS data. Modeling consists of a series ofanalytical relationships between various physical parameters, which allows for simulating DAS data efficiently based on various assumptions regarding the flow and wellbore. The forward model also allows us to characterize various mechanisms that introduce uncertainty to DAS data and various parameters estimated from DAS data. We also discuss methods to analyze DAS data to find the speed of sound and flow rates in the wellbore. The forward model enables us to develop efficient algorithms for the inverse model, including the wavelet transform and artificial neural networks to analyze DAS data in real time and calculate the flow rate and flow patterns in the wellbore.