Linking the Researchers, Developing the Innovations Manuscripts submittal opens till 15th August, 2017. Please submit your papers at editor@kwpublisher.com or editorkwpublisher@gmail.com

  • Volume 2017

    Validation of an Estimated Gas Condensate Reserve using Applied Uncertainty Analysis for the Condensate Reservoir Properties
    (International Journal of Engineering Works)

    Vol. 4, Issue 1, PP. 21-28, January 2017
    DOI
    Keywords: Probalistic, Uncertainty, Gas Condensate, Reserve, Distribution

    Download PDF

    Abstract

    The Monte Carlo technique has been used quite extensively in the exploration business but to a much lesser degree in reserve estimation and production forecasting. Whether those forecasts or estimation are made with detailed reservoir simulation, enough production history data or decline curve techniques, there will be uncertainty in the forecasts. The Monte Carlo method performs random sampling from probability functions which describe the uncertainty of various input parameters in the OHIP (Original Hydrocarbon In Place) mathematical model. Therefore, use of the probabilistic approach is superior in green fields rather than brown fields because it captures the full range of reality and where models are not yet calibrated to dynamic data. In this study, a Monte Carlo simulation model integrated in MBAL software was run for a deep heterogeneous gas condensate field in Niger Delta. This field was separated into two major fault blocks. The study captures phase behavior of gas-condensate systems under isothermal depletion and also requirements for accurate estimation of reservoir properties of zones bearing gas-condensate systems.The result from the simulation shows Monte Carlo probabilistic P50 case which are; 110Bscf of gas and 16mmstb of condensate were approximately 13Bscf and 3.7MMstb greater than volumetric estimate from an Independent 3rd party company. The indication is that the values of parameters that determine the P50 case where more optimistic than the P90 and P10 case due to the closeness of the figures of P50 case and those estimated by the other party. This method is not only more flexible in dealing with uncertainties but is also more advantageous for providing a better basis for investment decisions.

    Author

    1. Nnakaihe S.E: Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria 
    2. Nwabia F.N: Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

    Full Text

    Cite

    Nnakaihe S.E, Nwabia F.N, "Validation of an Estimated Gas Condensate Reserve using Applied Uncertainty Analysis for the Condensate Reservoir Properties" International Journal of Engineering Works, Vol. 4, Issue 1, PP. 21-28, January 2017. 

    References

    1. [1]     www.petrobjects.com 2003-2004 Petrobjects.
    2. [2]     Fan, L., Harris, B., Jamaluddin, A., Kamath, J., Mott, R., Pope, G., Shandrygin, A. And Whitson, C.: 2005, Understanding Gas-Condensate Reservoirs, Oilfield Review, Schlumberger, Winter 2005/2006, Pp. 14-27.
    3. [3]     Tarek Ahmed 2006. Reservoir Engineering Hand Book. Copyright, Elsevier Inc. Gulf Publishing, 30 Corporate Drive. Suite 400. Burlington, Ma 01803, Usa 2006.
    4. [4]     Zhang H. R., Wheaton R. J. Condensate Banking Dynamics In Gas Condensate Fields: Changes In Produced Condensate To Gas Ratios. Spe Paper 64662 Presented At The Spe International Oil And Gas Conference And Exhibition, Beijing, China 2000; 7-10.
    5. [5]      Wheaton, R. J. And Zhang, H. R. 2000.  Condensate Banking Dynamics In Gas Condensate Fields: Compositional Changes And Condensate Accumulation Around Production Wells” Spe  Paper 62930 Presented At The Spe Annual Technical Conference And Exhibition , 1-4 Oct. Dallas, Usa.
    6. [6]      Fan Li, Harris Billy W, Jamaluddin  A – J, Kamath J, Mott R , Pope G – A, Shandygin A. Whitson C – H, 2005. Understanding Gas- Condensate Reservoirs, Oilfield Review 2005, 17(4), 14-27.
    7. [7]      Eissa M, Shokri El-M. Dewpoint Pressure Model For Gas Condensate Reservoirs Based On Genetic Programming, Cipc/Spe Gas Technology Symposium 2008 Joint Conference, Calgary, Alberta, Canada 2008; 16-19.
    8. [8]     Savary D. 1990. Gas Condensate Reservoir Evaluation Using An Equation Of State Pvt Package, Middle East Oil Show, Bahrain 1990; 16-19.
    9. [9]     Dawe R – A , Grattoni C- A. Fluid Flow Behavior Of Gas-Condensate And Near-Miscible Fluids At Pore Scale. Journal Of Petroleum Science And Engineering 2007.
    10. [10]  Thomas F – B, Bennion D -W, Zhou X - L, 1995. Towards Optimizing Gas Condensate Reservoir. Petroleum Society Of Cim And Canmet 1995; 95-09.
    11. [11]   Cho S – J, Civan F,  Starling K E. A Correlation To Predict Maximum Condensation For Retrograde Condensation Fluids And Its Use In Pressure Depletion Calculations. Spe Paper 14268, 1985
    12. [12]  Olaberinjo A – F, Oyewola M- O, Obiyemi O – A, Adeyanju O –A, Adaramola M-S. Kpim Of Gas Condensate Productivity: Prediction Of Condensate/Gas Ratio Using Reservoir Volumetric Balance. Journal Of Applied Sciences2006; 6(15): 3068-3074.
    13. [13]  J. Willets Et Al: “A Programme To Calculate Oil, Gas And Condensate Recovery Factors Using Monte Carlo Techniques” (1997).
    14. [14]  C. R. Smith, G. W. Tracy And R. L. Farrar: “Applied Reservoir Engineering Vol1 And 2” (1999)
    15. [15]  Wiiliam G. Lyons And Garry J. P.: “Standard Handbook Of Petroleum And Natural Gas Engineering” (2005).
    16. [16]  Havlena D., And Odeh A. S.: “The Material Balance As An Equation Of A Straight Line, ”JPT (August 1963