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A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 400 producers and injectors. The production wells are deviated (slanted) or horizontal and have been completed in multiple formations. An empirical, full field reservoir management technology, based on a data-driven reservoir model was used for this study. The model was conditioned to all available types of field data (measurements) such as production and injection history, well configurations, well-head pressure, completion details, well logs, core analysis, time-lapse saturation logs, and well tests. The well tests were used to estimates the static reservoir pressure as a function of space and time. Time-lapse saturation (Pulse-Neutron) logs were available for a large number of wells indicating the state of water saturation in multiple locations in the reservoir at different times. The data-driven, full field model was trained and history matched using machine learning technology based on data from all wells between 1975 and 2001. The history matched model was deployed in predictive mode to generate (forecast) production from 2002 to 2010 and the results was compared with historical production (Blind History Match). Finally future production from the field (2011 to 2014) was forecasted. The main challenge in this study was to simultaneously history match static reservoir pressure, water saturation and production rates (constraining well-head pressure) for all the wells in the field. History matches on a well-by-well basis and for the entire asset is presented. The quality of the matches clearly demonstrates the value that can be added to any given asset using pattern recognition technologies to build empirical reservoir management tools. This model was used to identify infill locations and water injection schedule in this field. RESERVOIR MANAGEMENT Reservoir management has been defined as use of financial, technological, and human resources, to minimizing capital investments and operating expenses and to maximize economic recovery of oil and gas from a reservoir. The purpose of reservoir management is to control operations in order to obtain the maximum possible economic recovery from a reservoir on the basis of facts, information, and knowledge (Thakur 1996). Historically, tools that have been successfully and effectively used in reservoir management integrate geology, petrophysics, geophysics and petroleum engineering throughout the life cycle of a hydrocarbon asset. Through the use of technologies such as remote sensors and simulation modeling, reservoir management can improve production rates and increase the total amount of oil and gas recovered from a field (Chevron 2012). Reservoir simulation and modeling has proven to be one of the most effective instruments that can integrate data and expertise fr...

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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.