Time-lapse or 4D methodology, that uses repeated 3D seismic surveys to monitor fluids saturations changes in reservoir, is a recommended tool for reservoir management in particular in deepwater fields where additional investments must be carefully evaluated due to the associated costs.This paper presents a real case of a successful rejuvenation project of a deepwater field in West Africa, supported by integrated 4D and reservoir 3D studies. The field, located in 800 m of water depth, has been producing for 10 years with a rate currently equal to the half of the FPSO nameplate capacity. In 2010 a seismic acquisition specifically designed for 4D purposes was executed and, integrated with the historical production data, led to a revision of the levels' potentiality. Reservoir barriers and fluids movements, water and gas in particular generated by the injection, have been modeled based on 4D acoustic impedance differences and were supported by history matching on the dynamic model. The strongly integrated approach supported a volumes in place revision that drove a sidetrack campaign on two wells to reach undrained areas. The integrated study highlighted also the possible risk of water encroachment on one of the targets of the two wells, that was therefore considered as a secondary target in the sidetrack plan. The result of the drilling campaign, combined with near field exploration, confirmed the expectations and contributed to double the production back to the initial peak plateau rate. Lesson learnt on this successful integration among disciplines are presented.

This paper presents an innovative application of the Integrated Asset Model (IAM) approach for simulating a surface network collecting many fields on production and multiple constraints, using a last-generation High-Resolution Reservoir Simulator (HRRS) applied to low-permeability reservoirs and complex wells models. The reservoir models are directly coupled by a Field Manager (FM) process to an external network simulator. The presented approach is flexible and highly efficient, using the logic of a modern High-Resolution Reservoir Simulator integrated within a unique IAM model, where the network simulator acts as a cyclic constraints updater for the independent reservoirs in order to continuously account for flow assurance effects. The proposed method relies on HRRS modularity, characterized by the possibility of integrating a Field Manager process with multiple reservoirs simulation processes: for each time step, the former provides updated pressure constraints at Tubing Head and well allocations according to the defined strategy, the latter solves the reservoir equations for each model. The FM acts as an orchestrator for a variety of reservoirs and network simulation instances, allowing to change reservoir and network simulator type without modifying the development strategy. The network simulator computes pressure drops and temperature along the pipelines by appropriate multiphase correlations, tuned against the available measured data. The proposed flexible IAM approach was preliminary tested on a single reservoir model to optimize the computational efficiency with respect to the needed process details in terms of memory usage and simulation run-time. Then, the methodology was implemented on the full asset: three low-permeability reservoirs with horizontal multi-fractured wells interconnected to a complex surface network, constrained by limited gas market demand and zero flaring policy. The IAM approach provided a flexible method to analyze different development options and wells/pipelines routing configurations to maximize oil production, improving asset gas management. As a result, the three dynamic models were successfully coupled, honoring overall asset and facilities constraints. The comparison between the resulting production profiles with the standalone model simulations, constrained by fixed minimum Tubing Head Pressure (THP), clearly shows the effectiveness of the proposed IAM approach: being the THP calculated in IAM according to the actual flow conditions, the proposed methodology resulted in a strong improvement especially during tail-end production phase that impacts ultimate recovery and reserves estimation. With the proposed approach, the asset performance could be properly evaluated by correctly taking into account the backpressure of the multiple interdependent platforms. Moreover, the application of HRRS enables to run the reservoir simulations in an efficient way on a High Performance Computing (HPC) cluster to speed up the overall process.