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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

Reducing Uncertainties of in-Place Hydrocarbon Estimates through Proper Monte Carlo Simulation Design: A Case Study

Mohammad A. Al-Khalifa1

(1) Saudi Aramco, Dhahran, Saudi Arabia.

Monte Carlo simulation (MCS) is one of the commonly used methods that provide in-pace hydrocarbons (IPH) estimates with a measurable uncertainty. Any error in MCS design would lead to an error in the results. MCS methods are widely covered in the literature, but only few comprehensively cover all aspects of MCS design and its impact on the results. This paper is divided into two parts: theoretical and practical; to illustrate the importance of proper MCS design as applied to an actual case study.

In the theoretical part, the major steps of MCS design are discussed in detail and an optimum procedure is presented. Critical parts of the MCS design, such as number of Iterations and key input variables selection are presented. Also, dependencies between input parameters, ranking methods and results are defined.

In the second part, an actual case study is carried out where the theoretical part is applied to field data taken from an Australian gas field. Twelve cases were designed from the field data to test different MCS designs. Each case has different input data, distribution shape and dependencies. Also, two different probabilistic estimates ranking methods, which are the ‘(P10-P90)/P50’ and the ‘Coefficient Of Variance’ methods, were evaluated. The results indicate that fitting the distributing shape and truncating it based on the mean of the input properties gave IPH estimate that are higher and have lower uncertainties compared to the other cases. Therefore it can be concluded that a proper design of the Monte Carlo simulation results in the reduction of IPH estimates uncertainty.