Effect of Quick Salinity Variations in Virtual Meter Applied on Water Injection Wells

Abstract

Abstract Real Time Virtual Flowmeter (RTVFM) is a key digital technology for real time monitoring of well performances, for both production and injection wells. The main advantage of this tool is to provide estimations of well flow rates, based on wellbore pressure drop, using real-time (RT) pressure and temperature data measured by gauges installed in the well. This paper focuses on the effects of water properties on RTVFM application to water injection by evaluating their impact on the dynamic gradient and its implication in the rate estimation. Injected water can be a mix of different sources: sea water, fresh water, formation water and produced water. As a result of these different contributions, it is common to observe variations of salinity even on an hourly basis. A variation of water salinity impacts on density and viscosity, therefore changing the dynamic gradient. Salinity in injected water is commonly measured by sampling analysis, thus providing data with a much lower frequency than RT gauges. As a result, it is not usually possible to integrate salinity variation into the standard RTVFM workflow, leading to significant errors in the rate estimation. The innovative workflow presented in this paper, named Virtual Salinity, calculates water salinity in real time in wells equipped with reliable flowmeters. It regresses the dynamic gradient equation on salinity values. The results of this workflow improve the quality of RTVFM application to other wells injecting the same water mix. At each timestep, virtual salinity values are used to evaluate the correct pressure gradient for RTVFM calculation. The workflow has been successfully tested on a deepwater offshore asset, to prove its reliability. The Virtual Salinity has been applied on an offshore injection network: three wells injecting a mix of produced and sea water. The workflow, applied to all injectors, generated consistent salinity profiles. A reference virtual salinity profile has been used as an input for RTVFM simulations. For all of the injectors, RTVFM reproduced the independent flowmeter measurement with enough accuracy. The innovative methodology here presented provides a key tool to monitor salinity of injected water and can be used in field where injected salinity is not measured, providing a valuable information at minimal costs. Water salinity is one of the main inputs of production data analysis, that allows to maximize reservoir knowledge and consequently final recovery. Moreover, the greater accuracy of Virtual Meter rates significantly improves the injection monitoring, thus supporting an effective reservoir management.