Effect of Systematic and Random Flow Measurement Errors on History Matching

History matching is the process of modifying a numerical model (representing a reservoir) using observed data. In the oil industry, production data is employed during history matching to reduce the uncertainty in reservoir models. However, production data, which is normally measured by flowmeters or estimated by mathematical equations, inevitably has inherent errors. In other words, the data which is used to reduce the uncertainty of the model has some uncertainty in itself. 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. Fourteen production data sets with different ranges of systematic and random error, between 0% and 10%, have been employed in history matching and the results subsequently evaluated based on a reference model. The results show that systematic error considerably affects history matching while the effect of random error on the considered scenarios is seen to be insignificant. Amongst the parameters which were estimated (porosity, permeability, oil and gas production), permeability was seen to be the most sensitive to flow measurement error. Finally, considering the noticeable effect of systematic error, it is suggested to undertake flowmeter calibration and maintenance regularly, although the cost could be high.