Optimizing Production in a Deepwater Brazilian Field Through Enhanced Decision-Making with Integrated Production Systems Modelling

Deepwater projects are complex developments where the cost of change can be very high. In this study, a novel methodology is proposed to enhance decision-making using integrated modelling. This approach enables a rapid and accurate representation of entire networks, effectively defining uncertainties and highlighting the benefits of integrating subsurface elements with network facilities. The methodology involves constructing a base Integrated Production System Model (IPSM), and defining ranges for uncertainties, which may include geological, fluid, rock-fluid interactions, and network equipment conditions (e.g., tubing, risers, pumps, etc.). To manage the high variability and complexity of potential scenarios, an efficient sampling technique was combined with an optimization strategy, enabling a swift identification of global maximums. The results are presented as time-series data for each network node, accompanied by visualizations of key properties (pressure, flow rates, etc.) in the entire network plot, which facilitate bottleneck identification in the coupled model. The current approach was applied to Atlanta deep offshore field in Santos Basin, Brazil. After establishing the base integrated model, uncertainties within both the model and network were readily defined and managed. The simulation running time only increased by 12% using this approach, despite integrating two reservoirs and the production network. The sampling method proved effective, reducing the required number of models to thoroughly explore the search space. With fewer than 80 simulations, it was possible to achieve a 21% increase in cumulative oil production, supporting rapid and precise decision-making around flexible and uncertain operational parameters. This approach not only pinpoints optimal values for each flexible parameter but also identifies the most impactful ones, aiding prioritization based on cost-effectiveness and implementation feasibility. The methodology significantly streamlines the optimization of coupled models, which is crucial in scenarios where late modifications are expensive and challenging. This approach is particularly beneficial for high fidelity models that integrate diverse reservoirs and multiple fluid types within the same facility. It enhances accuracy and reduces project risks by ensuring efficient decision-making processes.