Advanced Thermo-Fluid Dynamic Well Model for RTVFM Flow Rates Estimation

Abstract

The availability of a simple and robust flow allocation system is of primary importance for reservoir management since it provides oil, water, and gas production for each well. The low frequency of well separator tests and the difficulties in performing regular maintenance of multiphase flow meters have led to the development of Real Time Virtual Flow Meter (RTVFM) in Eni, a numerical solution to obtain real time flow rate estimation from pressure/temperature gauges measurements. This paper discusses the implementation and application of a novel RTVFM algorithm that increases the accuracy, stability, and robustness of the existing numerical tools even in case of extreme oil field environment with significant uncertainties. Current Virtual Meter algorithms are based on fluid dynamic simulators which calculate the pressure drops through wellbore, choke, and flowlines; the algorithm can be run in real time to find the optimal production rates that minimize the error between physical pressure readings and the calculated ones. In this work, a constraint is added to the system by including the temperature matching in the objective function, further improving the tool reliability. An accurate heat transfer characterization of the well has been implemented to predict the temperature changes along the wellbore during time, as well as the thermal effect due to pressure variations (Joule-Thompson effect). The effectiveness of the implemented algorithm has been proven by its application on a few offshore oil producers. In the chosen wells, equipped with dedicated MPFMs, the production measurements are not always reliable and RTVFM can be a valid support tool for back allocation. However, the flow rate estimation can be affected by significant uncertainties like production parameters variability (water cut and gas oil ratio) and fluid properties variation due to gas re-injection or artificial gas lift. In this scenario, the proposed enthalpy balance model allows to find a unique solution for the flow rate estimation, while the algorithm based only on pressure readings can converge to multiple solution rates. Increasing the accuracy of RTVFM tool is imperative to allow a reliable back allocation process, even in case of MPFM unavailability, poor sensors data quality and highly variable fluid properties. This paper investigated how an advanced thermo-fluid dynamic model can improve Virtual Meter algorithms, thus reducing the uncertainties in the numerical flow rate estimation.