Abstract
Current trends in hydrocarbon production are driven by improved oilfield management with various control and optimization strategies. These strategies rely on the efficiency of monitoring equipment which is used to obtain real-time oil and gas production rates with sufficient spatial and temporal resolution. Consequently considerable efforts have been put in the development of reliable flow measurement techniques dedicated to real-time monitoring of hydrocarbon flow rates without separation of the phases.
Multiphase metering at an upper limit of gas volume fraction (mostly above 95% GVF) is referred to as wet gas metering. Usually the metering of a wet gas flow is performed using standard dry gas meters which are supplemented by additional corrections in order to account for the impact of a liquid phase. These correction algorithms use explicit information on the measured fractions of oil, water and gas and can either be based on empirical correlations or employ mechanistic flow models. In contrast, issues related to installation effects of various components (such as pressure/temperature gauges and ad-hoc sensors) cannot usually be handled using simple flow models and should be assessed using extensive experimental analysis.
As a general solution, more fundamental physical background is put into the design and development of a new generation of a wet gas meter. The usage of general flow models both on micro and macro scales reduces uncertainty relevant to empirical modelling and promises more robust flow metering performance. In this work, the issues related to the optimal salinity sensor location have been studied via computational fluid dynamics. In particular, different variants of sensor placement scenarios have been analyzed in order to identify the location, which will have the maximum water volume fraction to achieve as high sensitivity as possible.
