Quantifying the uncertainty in the volumetric estimation of original oil in place (OOIP) is an important process in evaluating the field potential and hence in designing the proper and the most economical subsurface and surface facilities to produce the field reserves. This uncertainty in the OOIP estimate results from uncertainty in reservoir areal extent, net reservoir thickness, porosity, and hydrocarbon saturation. In this work, a methodology is presented to assess the uncertainty in the hydrocarbon saturation estimated from open hole logs using the commonly used empirical and theoretical shaly sand models. This technique is based on development of water saturation error analysis charts for the commonly used water saturation models (Simandoux, Indonesian, Waxman & Smits, Dual Water, and Effective Medium) due to the uncertainty in the different input parameters to each model separately. Both analytical and numerical error analysis techniques were used to develop these charts and hence used as a forward tool to quantify the uncertainty in the hydrocarbon saturation due to the uncertainty in the petrophysical and electrical rock properties. Fifteen wells with 1300 shaly sand points from Alam Bewab formation, in Western desert of Egypt, were used as our data base in generating these error analysis charts. The uncertainty in input data was assumed from ± 5 to ± 15%. The results showed a significant range of uncertainty in hydrocarbon saturation estimate from ± 0.3% reaching to ± 85% in some models. General water saturation error analysis charts were developed, for each model, based on the above mentioned database and validated mathematically. These charts can be easily used to predict the uncertainty in hydrocarbon saturation estimate due to uncertainty in the input data. In addition, it can be considered as a useful screening tool to select the best saturation model to be used depending on the input data uncertainty.

Quantifying the uncertainty in the volumetric estimation of original oil in place (OOIP) is an important process in evaluating the field potential and hence in designing the proper and the most economical subsurface and surface facilities to produce the field reserves. This uncertainty in the OOIP estimate results from uncertainty in reservoir areal extent, net reservoir thickness, porosity, and hydrocarbon saturation. In this work, a methodology is presented to assess the uncertainty in the hydrocarbon saturation estimated from open hole logs using the commonly used empirical and theoretical shaly sand models. This technique is based on development of water saturation sensitivity charts for the most commonly used water saturation models (Simandoux, Indonesian, Waxman & Smits, Dual Water, and Effective Medium) due to the uncertainty in the different input parameters to each model separately. Both analytical and numerical error analysis techniques were used to develop these charts and hence used as a forward tool to quantify the uncertainty in the hydrocarbon saturation due to the uncertainty in the core and shale properties. Fifteen wells with 1300 shaly sand points from Alam Bewab formation, in Western desert of Egypt, were used as the data base in generating these sensitivity charts. The uncertainty in input data was assumed from ± 5 to ± 15%. The results showed a significant range of uncertainty in hydrocarbon saturation estimate from ± 2% reaching to ± 75% in some models. General water saturation sensitivity charts were developed, for each model, based on the above mentioned database and validated mathematically. These charts can be easily used to predict the uncertainty in hydrocarbon saturation estimate due to uncertainty in the input data. In addition, it can be considered as a useful screening tool to select the best saturation model to be used depending on the input data uncertainty. Introduction The electric log in the oil industry is the most used tool for in-situ detection of hydrocarbon accumulations. However, quantitative measurement of oil in place is not simple, because calculation of the oil saturation of a reservoir from electric log responses is complicated by the presence of clay minerals in the pore network. The complexity is twofold. Firstly, the clay geometry probably influences the conduction of the electric logs. Secondly, the clay minerals have ability to conduct electricity through ion-exchange reactions.1 The conventional interpretation technique will not give accurate water saturations for the shaly sand formations, as it assumed that the only conductive phase in the formation is the interstitial water and considered the rock as insulator. In the presence of shale the rock becomes conductive and adds excess conductivity to the interstitial water conductivity, so more than thirty shaly sand models have been developed in the literature to get accurate water saturations taking into account the excess conductivity due to shale.2, 3, 4 The dependency of the different developed shaly sand models on their input parameters has a great impact on choosing the best and suitable model to be used. The uncertainty in the input parameters for the different shaly sand models will complicate the interpretation for a specific shaly sand formation as using improper model that highly depends on these uncertain parameters may result in bypassing the pay zones.