Dynamic Reservoir Geological Mapping Using Generative AI and Satellite Data

Abstract Reservoir characteristics are fundamental for optimizing hydrocarbon recovery and ensuring effective reservoir management. Traditional methods for creating geological maps face Challenges such as high costs, lengthy timelines, and limited real-time data. This study aims to Develop a novel approach by integrating high-resolution satellite data with Generative. Adversarial Networks (GANs) to create real-time geological maps and predict subsurface. Patterns, addressing these limitations, and enhancing reservoir management. The methodology involved integrating satellite imagery, seismic data, and well logs to train. GAN models that can identify faults, fractures, and subsurface changes. The approach also Incorporated time-series satellite data to track reservoir dynamics over time. The workflow included preprocessing the data, training and validating the GAN model, and integrating Geospatial data. The final model was tested and validated with real-world field data to ensure its Practical applicability in industrial settings. Preliminary results showed a 20% improvement in fault and fracture detection accuracy. Compared to traditional methods. Additionally, machine learning models achieved a detection sensitivity of 93%, significantly reducing false positives and enhancing fracture mapping. Precision. Key parameters such as reservoir temperature and in-situ stress contributed to improved model accuracy, with temperature variations accounting for a 25% difference in Predictions. The integration of fluid viscosity data further enhanced model reliability, leading to A 15% increase in prediction consistency across diverse reservoir conditions. This study demonstrates the potential of using Generative AI to transform the reservoir characterization, offering a scalable, cost-effective, and eco-friendly solution for dynamic reservoir management.