Abstract
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.
Highlights
The knowledge of reservoir management has dramatically improved
The plots clearly illustrate the substantial effect of systematic errors on history matching, with a contradictory suggestion that the effect of random errors is insignificant
The results of the study clearly show the considerable effect of systematic flow measurement errors on the results of history matching
Summary
The knowledge of reservoir management has dramatically improved. Managing hydrocarbon reservoirs to maximise the profit from them, which had a limited knowledge and involved just simple calculations in the early years of the oil and gas industry, has become a complicated dynamic process of setting goals, decision making, implementing, Fluid and Complex Systems Research Centre, Coventry University, Coventry, UKAtout Process Limited, Southampton, UK monitoring, analysing data, and modifying decisions (Satter et al 1994). The knowledge of reservoir management has dramatically improved. Managing hydrocarbon reservoirs to maximise the profit from them, which had a limited knowledge and involved just simple calculations in the early years of the oil and gas industry, has become a complicated dynamic process of setting goals, decision making, implementing, Fluid and Complex Systems Research Centre, Coventry University, Coventry, UK. Atout Process Limited, Southampton, UK monitoring, analysing data, and modifying decisions (Satter et al 1994). Reservoir management in its present form needs a multidisciplinary approach and the integrated application of different technologies and professional software. In this process, a large amount of data are recorded and analysed and engineers need to deal with numerous uncertainties. CLRM (Fig. 1) is a combination of history matching and model-based optimisation, and its aim is Journal of Petroleum Exploration and Production Technology (2019) 9:2853–2862
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