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Journal of Porous Media
IF: 1.49 5-Year IF: 1.159 SJR: 0.43 SNIP: 0.671 CiteScore™: 1.58

ISSN Print: 1091-028X
ISSN Online: 1934-0508

Volume 23, 2020 Volume 22, 2019 Volume 21, 2018 Volume 20, 2017 Volume 19, 2016 Volume 18, 2015 Volume 17, 2014 Volume 16, 2013 Volume 15, 2012 Volume 14, 2011 Volume 13, 2010 Volume 12, 2009 Volume 11, 2008 Volume 10, 2007 Volume 9, 2006 Volume 8, 2005 Volume 7, 2004 Volume 6, 2003 Volume 5, 2002 Volume 4, 2001 Volume 3, 2000 Volume 2, 1999 Volume 1, 1998

Journal of Porous Media

DOI: 10.1615/JPorMedia.2019025067
pages 957-973


Craig D. Marshall
National Engineering Laboratory (NEL), Glasgow, UK; Fluid and Complex Systems Research Centre, Coventry University, Coventry, UK
Mahdi Sadri
Fluid and Complex Systems Research Centre, Coventry University, Coventry, UK
Hamidreza Hamdi
University of Calgary, Calgary, Canada
Seyed M. Shariatipour
Fluid and Complex Systems Research Centre, Coventry University, Coventry, UK
Wai Kong Lee
National Engineering Laboratory (NEL), Glasgow, UK
Alun L. Thomas
National Engineering Laboratory (NEL), Glasgow, UK
James Shaw-Stewart
University of Warwick, Coventry, UK


In terms of maximizing economic recovery (MER) in the United Kingdom Continental Shelf (UKCS), the measurement of well production rates is essential to optimize the hydrocarbon production strategy from within the well itself. This is achieved through a process called a well test in which a snapshot of production is monitored by measurement equipment and instrumentation. The data collected are then used to characterize the reservoir near the wells and to optimize the wells' production rates. However, the measurement accuracy required 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 modeling and reservoir management are highly dependent on these measurements, the allowable uncertainty must be known to fully assess whether the equipment and the methodology of verifying the measurements are 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 emphasized.


  1. Abdolhosseini, H. and Khamehchi, E., History Matching Using Traditional and Finite Size Ensemble Kalman Filter, J. Natural Gas Sci. Eng., vol. 27, pp. 1748-1757, 2015.

  2. Abdollahzadeh, A., et al., Bayesian Optimization Algorithm Applied to Uncertainty Quantification, SPE J, vol. 17, no. 3, pp. 865-873,2012.

  3. Adams, C., Kavanagh, M., and Tighe, C., North Sea Oil: That Sinking Feeling, Financial Times, February 25, 2015.

  4. Ahmadi, R., Aminshahidy, B., and Shahrabi, J., Well-Testing Model Identification Using Time-Series Shapelets, J. Petrol. Sci. Eng., vol. 149, pp. 292-305, 2017.

  5. Barbe, J. and Boyd, B., Short-Term Buildup Testing, J. Petrol. Technol., vol. 23, no. 7, pp. 800-804, 1971.

  6. Bottomley, W., Schouten, J., McDonald, E., and Cooney, T., Novel Well Test Design for the Evaluation of Complete Well Permeability and Productivity for CSG Wells in the Surat Basin, J. Natural Gas Sci. Eng., vol. 33, pp. 1002-1009, 2016.

  7. Bourdet, D., Well Test Analysis: The Use of Advanced Interpretation Models, Amsterdam: Elsevier, 2002.

  8. Chase, R., Improved Estimation of Gas Well Deliverability from Single-Point Tests, J. Canadian Petrol. Technol., vol. 41, no. 11, 2002. DOI: 10.2118/2000-012.

  9. Earlougher, R.C., Advances in Well Test Analysis, SPE Monograph Series, No. 5, Dallas, TX: Society of Petroleum Engineers, 1977.

  10. Emerson Automation Solutions, PID Control in 3-Phase Oil and Gas Separation, accessed February 12, 2017, from, 2014.

  11. Falcone, G., Hewitt, G., Alimonti, C., and Harrison, B., Multiphase Flow Metering: Current Trends and Future Developments, SPE Annual Technical Conf. and Exhibition, Richardson, TX, 2001.

  12. Foster, G., Wong, D., and Asgarpour, S., The Use of Pressure Build-Up Data in Pressure Transient Testing, J. Canadian Petrol. Technol., vol. 28, no. 6,1989. DOI: 10.2118/89-06-10.

  13. Hajizadeh, Y., Christie, M., and Demyanov, V., Ant Colony Optimization for History Matching and Uncertainty Quantification of Reservoir Models, J. Petrol. Sci. Eng., vol. 77, no. 1, pp. 78-92, 2011.

  14. Hamdi, H., Well-Test Response in Stochastic Permeable Media, J. Petrol. Sci. Eng., vol. 119, pp. 169-184, 2014.

  15. Hegeman, P.S., Hallford, D.L., and Joseph, J.A., Well Test Analysis with Changing Wellbore Storage, SPE Formation Eval., vol. 8, no. 3, pp. 201-207,1993.

  16. HM Revenue and Customs, Government Revenues from UK Oil and Gas Production, London, 2014.

  17. HM Treasury, Driving Investment: A Plan to Reform the Oil and Gas Fiscal Regime, London, 2014.

  18. Houze, O., et al., Dynamic Data Analysis: The Theory and Practice of Pressure Transient, Production Analysis, Well Performance Analysis, Production Logging and the Use of Permanent Downhole Gauge Data, Houston, TX: KAPPA Engineering, 2015.

  19. Khosravi, V. and Ketabi, S., Well Test Analysis of Gas Condensate Reservoirs from Pressure Build Up and Draw Down Tests, Offshore Technology Conf., Kuala Lumpur, Malaysia, March 25-28, 2014.

  20. Lake, L. and Walsh, M., A Generalized Approach to Primary Hydrocarbon Recovery of Petroleum Exploration and Production, Amsterdam: Elsevier Science BV, 2003.

  21. Lee, J., Well Testing, New York: Society of Petroleum Engineers, 1982.

  22. Lindsay, G., Hay, J., Glen, N., and Shariatipour, S., Profiling and Trending of Coriolis Meter Secondary Process Value Drift due to Ambient Temperature Fluctuations, Flow Measure. Instrument., vol. 59, pp. 225-232, 2017.

  23. Matthews, C.S. and Russell, D.G., Pressure Buildup and Flow Tests in Wells, SPE Monograph Series, Dallas, TX: Society of Petroleum Engineers, 1967.

  24. McAleese, S., Operational Aspects of Oil and Gas Well Testing, Amsterdam: Elsevier, 2000.

  25. Meunier, D., Wittmann, M., and Stewart, G., Interpretation of Pressure Buildup Test Using In-Situ Measurement of Afterflow, J. Petrol. Technol, vol. 37, no. 1,pp. 143-152, 1985.

  26. Nezhad, M.M., Javadi, A., and Rezania, M., Modeling of Contaminant Transport in Soils Considering the Effects of Micro- and Macro-Heterogeneity, J. Hydrology, vol. 404, nos. 3-4, pp. 332-338, 2011.

  27. Office for Budget Responsibility, Fiscal Sustainabality Report, London, 2014.

  28. Oil and Gas Authority, Guidance Notes for Petroleum Measurement, Aberdeen, UK, 2015.

  29. Ramy, H., Kumar, A., and Gulati, M.S., Oil Well Test Analysis under Water-Drive Conditions, Arlington, TX: American Gas Association, 1973.

  30. Ross, A., Well Testing - An Evaluation of Test Separators and Multiphase Meters, 28th International North Sea Flow Measurement Workshop, London, pp. 214-235, 2010.

  31. Rumbles, L., Importance of Flow Measurement for Separators, accessed November 3, 2017, from, 2014.

  32. Sadri, M., Shariatipour, S., and Hunt, A., Effects of Flow Measurement Errors on Oil and Gas Production Forecasts, Comput. Experiment. Methods Multiphase Complex FlowIX, vol. 115, p. 133, 2017.

  33. Sadri, M., Shariatipour, S., and Hunt, A., Effect of Systematic and Random Flow Measurement Errors on History Matching, The 5th Int. Conf. on Oil and Gas Engineering and Technology, Kuala Lumpur, Malaysia, 2018.

  34. Scheidt, C. and Caers, J., Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods-Application to a West Africa Deepwater Turbidite Reservoir, SPEJ., vol. 14, no. 4, pp. 680-692, 2009.

  35. Stewart, G., Well Test Design and Analysis, Tulsa, OK: PennWell Corporation, 2011.

  36. Terry, R.E., Rogers, J.B., and Craft, B.C., Applied Petroleum Reservoir Engineering, Upper Saddle River, NJ: Pearson Education, 2013.

  37. U.S. Energy Information Administration, North Sea Oil Production, 2016.

  38. van Golf-Racht, T.D., Fundamentals of Fractured Reservoir Engineering, Amsterdam: Elsevier, 1982.

  39. Wood, I., UKCS Maximising Recovery Review: Final Report, 2014.

  40. Zeng, L., Chang, H., and Zhang, D., A Probabilistic Collocation-Based Kalman Filter for History Matching, SPE J., vol. 16, no. 2, pp. 294-306,2011.

  41. Zhao, H., et al., History Matching and Production Optimization of Water Flooding based on a Data-Driven Interwell Numerical Simulation Model, J. Natural Gas Sci. Eng., vol. 31, pp. 48-66, 2016.

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