Validation Of An Estimated Gas Condensate Reserve Using Applied Uncertainty Analysis For The Condensate Reservoir Properties
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
... ters 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.