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Thick reservoirs usually have variations of PVT properties such as bubble point pressure and dissolved gas oil ratio with depth. A considerable effort usually spent to describe such properties to be used in dynamic models either in Black Oil or compositional simulators. However, during predictions many dynamic models set the BHP constraint as a constant value for all producers in the entire reservoir regardless of perforations depth. Usually this constant value is close to the average bubble point pressure or a little higher to ensure single phase (oil) production and not considering changing of bubble point pressure with depth. The above procedure introduces uncertainty regarding well potential, which involves a risk of not presenting accurate well performance in the simulator and would impact oil production and recovery forecasts. The proposed method was developed to calculate following BHP constraint based on bubble point pressure versus depth relationship for each grid cell in the dynamic model, thus creating 4D-array (I, J, K, BHP constraint) for the entire reservoir. The proposed method is fully automated and tested to assign BHP constrains in schedule section of huge dynamic model (2.6 million cells) with 600+ wells. Two prediction cases were tested (each case run twice, one run with constant BHP constraint for all the produces and the other one using variable BHP constraint recommended by the proposed method) to evaluate the impact of BHP constraint on production and oil recovery forecasts. The results of these two runs showed a considerable difference in length of oil production plateau and oil recovery forecast, but negligible differences in gas oil ratio. The proposed method could be used for any dynamic model with vertical and/or horizontal variation of bubble point pressure to calculate an optimum BHP and thus WHP to maximize production rate without the risk of introducing gas phase at reservoir conditions. Introduction Thick reservoirs usually have variation of PVT properties with depth. Lighter reservoir fluid on top of the reservoir exhibits higher bubble point pressure (Pb) since it contains lighter hydrocarbon components and thus lower density. However, at the bottom of the reservoir exhibits lower bubble point pressure since it contains heavier hydrocarbons and thus higher density. This compositional gradient should be modeled correctly using PVT data and incorporated it in the dynamic model for better model optimization and predictions. The subject reservoir is a thick lower cretaceous carbonate buildup with a maximum thickness about 500 feet at the crest. The carbonate formation presented here is the most prolific and geologically complex oil reservoir and it is one of ADCO's oil fields in UAE. It is undersaturated reservoir contains light oil with 37o API. The field has about 600+ wells and still on production for the last 44 years. During production history many PVT samples were collected from several producers. In this study, all PVT data were screened and only bottom hole samples were considered to generate bubble point versus depth trend. This trend is used in full field Blackoil dynamic model with 2.6 million cells. The model was initialized using log-derived J-functions based on reservoir rock types (1). Then the model was history-matched and currently is used for short-term and log-term development plan options. In the prediction cases a minimum BHP is specified in the well data control card to insure that the well will not produce below this limit. In the past a single value of BHP limit (Pb+SF) was used for all producers regardless of perforations depth which assumes a constant Pb pressure cross the reservoir. This approach could under estimate some production potential from some wells which have Pb much below this single value and/or introduce the risk of producing below Pb from some wells which have Pb above this single value. The proposed method takes into account the variation of Pb with depth in order to assign proper value for the minimum BHP constraint for each producer based on perforations depth.

Calculation of initial fluid saturations is a critical step in any 3D reservoir modeling studies. The initial water saturation (Swi) distribution will dictate the original oil in place (STOIP) estimation and will influence the subsequent steps in dynamic modeling (history match and predictions). Complex carbonate reservoirs always represent a quit a challenge to geologist and reservoir engineers to calculate the initial water saturation with limited or no SCAL data available. The proposed method in this study combines core data (permeability) from 32 cored wells with identifiable reservoir rock types (RRTs) and log data (porosity and Swi) to develop drainage log-derived capillary pressure (Pc) based on rock quality index (RQI) and then calculateJ-function for each RRT which was used to calculate the initial water saturation in the reservoir. The initialization results of the dynamic model indicate good Swi profile match between the calculated Swi and the log-Swi for 70 wells across the field. The calculation of STOIP indicates a good agreement (within 3% difference) between the geological 3D model (31 million cells fine scale) and the upscaled dynamic model (1 million cells). The proposed method can be used in any heterogeneous media to calculate initial fluid saturations. Introduction: Reservoir characterization is an essential part of building robust dynamic models for proper reservoir management and making reliable predictions. A good definition of reservoir rock types should relate somehow the geological facies to their petrophysical properties. However, this was not the case in this work there is an overlap of petrophysical properties between the different RRTs. It was difficult to differentiate between the Mercury injection capillary curves (MIPCs) for a given RRT based of porosity and/or permeability ranges. Besides, the Mercury displacing air in the MIPc measurements does not represent the correct displacement mechanism in the reservoir. The objectives of this study were:Develop log-derived Pcs or J-functions using the available data (log-porosity, log-Swi, core permeability, and log-derived RRTs) to calculate the initial water saturation distribution in the entire reservoir.Validate the results by comparing the log-derived Pc with measured-Pc by using porous-plate method (air/brine system) from selected plus with different RRTs.Most important is to match the calculated Swi with log-Swi profile from several wells across the field and calculate the original oil in place (STOIP). A dynamic model was constructed by upscaling a 3-D geological model, 31 million cells, of the Lower Cretaceous Carbonate buildup in one of ADCO's oil fields in UAE. The carbonate formation presented here is the most prolific and geologically complex oil reservoir. 17 Reservoir Rock Types (RRTs) were described based on facies, porosity and permeability. Log derived permeability based on a Neural Network (3), honoring the core permeability was used in the 3D geological model. 30 faults and an areal distribution of a dense RRT were incorporated into the model based on seismic data interpretation. Different simulation grids were realized to preserve the geological heterogeneity and the RRTs after upscaling of the geological model and the dynamic model was optimized to minimize the run time. Due to lack of pre-depletion RFTs, the free water level (FWL) was estimated from early pressure data (BHCIPs) and water saturation log from key transition zone wells. Data Preparation: All the core and log data from the 32 cored wells were filtered according to RRTs in a table format using EXCEL spread sheet software (see Table 1). The oil and water densities were measured at reservoir temperature (PVT data). The interfacial tension (IFT) between oil and brine also measured and assuming a contact angle of zero degree.