History matching is the process of modifying a numerical model (representing a reservoir) in the light of observed production data. In the oil and gas industry, production data are employed during a history matching exercise in order to reduce the uncertainty in associated reservoir models. However, production data, normally measured using commercial flowmeters that may or may not be accurate depending on factors such as maintenance schedules, or estimated using mathematical equations, inevitably has inherent errors. In other words, the data which are used to reduce the uncertainty of the model may have considerable uncertainty in itself. This problem is exacerbated for gas condensate and wet gas reservoirs as there are even greater errors associated with measuring small fractions of liquid. The influence of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this paper, the effect of systematic and random flow measurement errors on history matching is investigated. Initially, 14 production data sets with different ranges of systematic and random errors, from 0 to 10%, have been employed in a history matching exercise for an oil reservoir and the results have later been evaluated based on a reference model. Subsequently, 23 data sets with errors ranging from 0 to 20% have been employed in the same process for a wet gas reservoir. The results show that for both cases systematic errors considerably affect history matching, while the effect of random errors on the considered scenarios is seen to be insignificant. Although reservoir model parameters in the wet gas reservoir were not as sensitive to the flow measurement errors as in the oil reservoir, for both cases, the future production forecast was significantly affected by the errors. Permeability was seen to be the most sensitive history matching parameter to the flow measurement errors in the oil reservoir, while for the wet gas reservoir, the most sensitive parameter was the forecast of future oil and gas production. Finally, considering the noticeable effect of systematic errors on both cases, it is suggested that flowmeter calibration and regular maintenance is prioritised, although the subsequent economic cost needs to be considered.

In terms of Maximising Economic Recovery (MER) in the United Kingdom Continental Shelf (UKCS), the measurement of well production rates is essential to best optimise the hydrocarbon production strategy from within the well itself. This is achieved through a process called a well test where a snapshot of production is monitored by measurement equipment and instrumentation. The data collected is then used to characterise the reservoir near the wells and to optimise the wells’ production rates. However, how accurate that measurement has to be to provide sufficient control has not been established and there is little information in the public domain that shows what the current typical operational measurement uncertainty is. Given that modelling and reservoir management is highly dependent on these measurements, the allowable uncertainty must be known to fully assess if the equipment and the methodology of verifying the measurements is fit for purpose. This paper details an investigation of the effects of flow measurement errors on interpreting well testing data and estimating the recoverable reserves. In addition, current MER strategies for the UKCS are discussed and the importance of using downhole flow measurement data in well testing for MER has been emphasised.