Effect of Systematic and Random Flow Measurement Errors on History Matching

Research output: Contribution to conferenceAbstract

Abstract

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.
Original languageEnglish
Publication statusAccepted/In press - 3 Apr 2018
EventThe 5th International Conference on Oil & Gas Engineering and Technology 2018 - Convention Centre, Kuala lumpur, Malaysia
Duration: 13 Aug 201814 Aug 2018
http://estcon.utp.edu.my/icoget/

Conference

ConferenceThe 5th International Conference on Oil & Gas Engineering and Technology 2018
Abbreviated titleICOGET 2018
CountryMalaysia
CityKuala lumpur
Period13/08/1814/08/18
Internet address

Fingerprint

Flow measurement
Measurement errors
Systematic errors
Random errors
Flowmeters
Numerical models
Porosity
Calibration
Uncertainty
Gases
Costs
Industry
Oils

Keywords

  • Flow Measurement
  • History Matching
  • Systematic Error
  • Random Error

Cite this

Sadri, M., Shariatipour, S. M., & Hunt, A. (Accepted/In press). Effect of Systematic and Random Flow Measurement Errors on History Matching. Abstract from The 5th International Conference on Oil & Gas Engineering and Technology 2018, Kuala lumpur, Malaysia.

Effect of Systematic and Random Flow Measurement Errors on History Matching. / Sadri, Mahdi; Shariatipour, Seyed Mohammad; Hunt, Andrew.

2018. Abstract from The 5th International Conference on Oil & Gas Engineering and Technology 2018, Kuala lumpur, Malaysia.

Research output: Contribution to conferenceAbstract

Sadri, M, Shariatipour, SM & Hunt, A 2018, 'Effect of Systematic and Random Flow Measurement Errors on History Matching' The 5th International Conference on Oil & Gas Engineering and Technology 2018, Kuala lumpur, Malaysia, 13/08/18 - 14/08/18, .
Sadri M, Shariatipour SM, Hunt A. Effect of Systematic and Random Flow Measurement Errors on History Matching. 2018. Abstract from The 5th International Conference on Oil & Gas Engineering and Technology 2018, Kuala lumpur, Malaysia.
Sadri, Mahdi ; Shariatipour, Seyed Mohammad ; Hunt, Andrew. / Effect of Systematic and Random Flow Measurement Errors on History Matching. Abstract from The 5th International Conference on Oil & Gas Engineering and Technology 2018, Kuala lumpur, Malaysia.
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N2 - 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.

AB - 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.

KW - Flow Measurement

KW - History Matching

KW - Systematic Error

KW - Random Error

M3 - Abstract

ER -