Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation

Abstract

Abstract The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks. Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management. The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support. In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management. The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data. AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment. The integration process includes data preprocessing, model training, and AR deployment. Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices. The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95. The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency. Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions. Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks. The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response. Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions. This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization. Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies. This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation. By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency. This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems.