Fast evaluation of reservoir performance is one of the main concerns for decision making. Additionally, lack of reservoir data is a big issue in performing numerical simulations and obtaining reasonable history matching results in a short period of time. Asset management tends to question and avoid cost and downtime related to data acquisition. Pressure and rate transient analyses (P&RTA), along extended periods of production, allows the characterization of reservoir and near wellbore features, however, P&RTA alone does not provide pressure and fluid distribution prediction beyond the wellbore, it requires the integration with reservoir simulation. This paper provides a practical application for integrating well performance models to derive sandface pressure and rate data, that would enable P&RTA and ultimately a full-field integrated reservoir model. P&RTA facilitates the identification of reservoir parameters leading to improve and expedite reservoir history matching, and to perform the evaluation of various production scenarios. Available real-time pressure and rate data is key to achieving these objectives. The applied workflow allows engineers to quickly identify uncertainties and opportunities to evaluate different field development strategies to maximize the ultimate oil recovery. The methodology was successfully applied in a reservoir study in South America. To achieve the goals, it was necessary to characterize the reservoir in terms of original oil in place, reservoir rock properties, compartmentalization and well performance history by well in each producing reservoir. Unstructured refined grid principles (Voronoi Grid) were applied to build a relatively small and simple model that considers all the required physics of the problem. The estimated reservoir properties from petrophysical analysis were validated against P&RTA thus honoring the near wellbore effects. The resulting model permitted the generation of key field development strategies considering additional well placement and completion technologies and best production operational practices, as well as the characterization of major uncertainties related with the reservoir-well system. As a result of this application, prediction forecasts in comparison with the base case scenario for all formations showed that optimal production and development strategy results in 16.47 % reduction of water produced, with a simultaneous 13.2 % increase in overall oil recovery and 10 % project profitability. A series of recommendations were also derived including: (i) a data acquisition plan to minimize the impact of uncertainties in the field development plan, (ii) guidelines for generating a more reliable and economically profitable field development plan and (iii) opportunities visualization to improve and enhance oil recovery by conducting by water, gas and polymer injection.

It is widely recognized by oil industry that reservoir characteristics such as natural heterogeneity, spatial variability of permeability, net pay, porosity and spatial distribution of oil and water saturations control the fluid flow in porous media, reservoir performance, development strategies and the economics returns of investments of field development plan implementations. Nonetheless, most of the time few data are available for projects and fast evaluation of reservoir performance is needed for decision making. Understand the impact of reservoir parameters variability over pressures and saturations history match require multiple realizations of numerical simulations. Managers tend to avoid costs and production losses related to data acquisition. Pressure and rate transient analyses (PTA&RTA), along extended production time, allows the reservoir characterization (drive mechanisms, boundaries conditions) and understand near wellbore characteristics, however, PTA&RTA alone does not provide pressure and fluid distribution prediction beyond the wellbore, it requires the integration with reservoir simulation. This paper provides an integrated reservoir characterization workflow which allows reservoir engineers to identify production/reservoir uncertainties and constraints to evaluate different field development strategies to maximize the ultimate oil recovery in a short period of time. Pressure Transient Analysis (PTA) address near wellbore effects and help to identify gas cap or aquifer strength, reservoir boundaries as well as recognizing sealing/non-sealing faults. For this project, static pressure was not available to perform history matching, but real time Pump Intake Pressure (PIP) was measured from Electric Submersible Pump sensors. Vertical lift performance curve was determined by using well completion data at the mid-point of perforation extrapolating PIP to bottomhole pressure (Pwf). Rate Transient Analysis (RTA) utilizes continuous production and flowing pressure data to characterize the reservoir and completion and it was key to understand reservoir performance, and provide insights for rapid and reliable history matching. This workflow was successfully applied in South America reservoir. To accomplish goals, it was necessary to perform a quick reservoir characterization and analysis - reservoir compartmentalization, PVT property distributions, rock properties distribution, and well performance history – to understand reservoir pressure and production behavior. Unstructured refined grid principles (Voronoi Grid) were applied to build a relatively small and simple model that considers all the required physics of the problem. The estimated reservoir properties from petrophysical analysis were validated against PTA&RTA thus honoring the near wellbore effects. The resulting model permitted the generation of key field development strategies considering additional well placement and completion technologies and best production operational practices, as well as the characterization of major uncertainties related with the reservoir-well system. Once model was initialized under a satisfactory level of tolerance (difference between the original oil in place determined by numerical reservoir model and the static model less than 2%) the history matching process began. Henceforth, a history matching workflow was generated to optimize the number of sensitivities and prioritize variable of greater significance. The history matching process improved pressure and production match and reduced CPU time approximately by 80% and 60% respectively. Aquifer strength for each formation was determined as a result of these analyses. The reservoir and production uncertainties allowed generation of a new surveillance plan to improve reservoir characterization and performance in the short term. Finally, a base case scenario was generated as a starting point to determine the recovery factor and identify new opportunities to develop oil and gas recoverable reserves of the field. An optimum scenario was generated to reduce water production and increase oil recovery for each formation resulting in 17.6 % reduction of produced water, with a simultaneous 13 % increase in overall oil recovery and 8 % project profitability. Additionally a series of recommendations were also derived regarding to: 1. Guidelines to generate a more reliable, consistent, and economically profitable field development plan, 2. Data acquisition plan to minimize impact of uncertainties in the field development plan, and 3. Visualization of improved oil recovery opportunities by carrying out gas, water, and EOR processes like polymer injection.