Effects of Flow Measurement Errors on Oil and Gas Production Forecasts

Sadri, Mahdi; Shariatipour, Seyed M.; Hunt, A.

doi:10.2495/mpf170141

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…In a previous study, we showed that flowmeters which have errors just in one direction (i.e. positive or negative) cause more errors in the results of history matching compared to when they have errors in both directions (Sadri et al 2017). In this paper, as a more general investigation, the effect of systematic and random flow measurement errors on history matching in an oil and a wet gas reservoir is addressed.…”

Section: Introductionmentioning

confidence: 96%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

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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.

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Section: Introductionmentioning

confidence: 96%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

Self Cite

View full text Add to dashboard Cite

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“…Correct measurement and interpretation of data output from flow metering technologies is key to production forecasting, custody transfer and fiscal metering [12], [13], [14], [15], [16].…”

Section: = ( ) ( )mentioning

confidence: 99%

Coriolis meter density errors induced by ambient air and fluid temperature differentials

Lindsay

Glen

Hay

et al. 2020

Flow Measurement and Instrumentation

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Coriolis metering technology is widely applied throughout industry. In addition to the mass flow rate, a Coriolis meter can measure fluid density based on the resonant frequency of the flow tube vibration. There is currently increasing interest in utilising this density measurement capability as the primary process value in applications such as precision control for fluid property conditioning, and fluid contamination monitoring.However, within these applications, ambient temperature variation can be significant.This paper details research data obtained using NEL's 'Very Low Flow' single-phase facility. The rig was modified to include a programmable temperature enclosure in which a Coriolis meter was installed. Two commercial meter models from the same manufacturer were tested. Both meters showed fluid density errors when subjected to fluctuations in the surrounding ambient air temperature. The fluid properties of the test medium were confirmed to be stable using NEL's UKAS standard reference instrumentation.Previous temperature effects research for Coriolis meters have focussed on the process fluid temperature and there is little published data on the effects of ambient temperature.1.

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“…Therefore, using MPFMs can indirectly increase the recovery of oil and gas. Sadri et al (2017) investigated the effect of flow measurement errors on the production forecast. They performed several history matches based on different sets of observed data with different ranges of measurement error and concluded that flow meters which either overestimate or underestimate the flow rate have a more negative effect on history matching compared to the flowmeters that have errors in both directions.…”

Section: Introductionmentioning

confidence: 99%