Well Control

Well control in reservoir engineering aims to optimize fluid flow and production by managing well settings and operations. Current research heavily emphasizes machine learning, employing architectures like neural operators, convolutional neural networks (including physics-informed and recurrent variants), and graph neural networks to create fast and accurate surrogate models for reservoir simulation, replacing computationally expensive traditional methods. These data-driven approaches are applied to problems such as well placement optimization, completion sequencing, and robust control under geological uncertainty, significantly accelerating reservoir management and potentially improving production efficiency. The resulting improvements in computational speed and optimization capabilities have substantial implications for both economic profitability and environmental sustainability in oil and gas extraction and carbon capture storage.