Revolutionizing Reservoir Management with Machine Learning: Enhancing Efficiency and Decision-Making in the Oil and Gas Industry

Reservoir management plays a critical part in the oil and gas sector, including crucial duties like determining uncertainty, enhancing production methods, and making well-informed decisions on field development. These activities frequently include modelling fluid behavior in subsurface reservoirs by running a large number of flow simulations. However, running these simulations can be computationally expensive, particularly for large-scale reservoir models with tens of thousands to millions of grid cells and a variety of fluid components. Although parallel computing has advanced, this computational cost has prevented reservoir modelling from being used more widely in the development of smart fields, where quick decision-making and effective resource use are critical. In order to address these issues, this research investigates a novel strategy that makes use of both supervised and unsupervised machine learning (ML) radically alter reservoir simulation. In order to make reservoir simulation a more usable and adaptable tool for a variety of applications, the main goal is to significantly reduce the computational overhead often associated with it. This work intends to accelerate the creation of approximative solutions for diverse reservoir scenarios by integrating ML techniques into the simulation pipeline, changing reservoir management practices. The successes and advancements ensure the benefits of artificial intelligence (AI) and man-made awareness techniques in terms of vast information storage capacities and high levels of mathematical calculation ability. The upstream and regions of the oil and gas industry are covered in this study along with a summary of several scientists' work on AI and computerized reasoning applications and limitations. This comprehensive framework's presence could effectively eliminate the element of chance and cost of upkeep. The turn of events and progress utilizing these arising advancements have become savvy and makes the judgment method simple and direct. The review is valuable to get to knowledge of various AI strategies to proclaim its application for particular assignment in oil and gas area. and maintenance fees. The course of events and progress using these recent innovations have become sophisticated, making the judgement process clear-cut and easy. The review is helpful in learning about different AI application tactics for specific tasks in the oil and gas industry.