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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.

The Inflow Performance Relationship (IPR) describes the behavior of the well's flowing pressure and production rate, which is an important tool in understanding the reservoir/well behavior and quantifying the production rate. The IPR is often required for designing well completion, optimizing well production, nodal analysis calculations, and designing artificial lift. Different IPR correlations exist today in the petroleum industry with the most commonly used models are that of Vogel's and Fetkovitch's. In addition to few analytical correlations, that usually suffers from limited applicability. In this work, a new model to predict the IPR curve was developed, using a new correlation that accurately describes the behavior the oil mobility as a function of the average reservoir pressure. This new correlation was obtained using 47 actual field cases in addition to several simulated tests. After the development of the new model, its validity was tested by comparing its accuracy with that of the most common IPR models such as Vogel, Fetkovitch, Wiggins, and Sukarno models. Twelve field cases were used for this comparison. The results of this comparison showed that: the new developed model gave the best accuracy with an average absolute error of 6.6 %, while the other common models are ranked, according to their accuracy in the follow

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