Abstract
Background: Effective reservoir management require integrating multiple geological and technological parameters to optimize decision-making. Traditional approaches, while useful, often struggle with the complexity and volume of reservoir data, highlighting the need for more advanced analytical methods. Aim: This article examines various methodologies for data-driven comparative analysis and its application for selection of drilling points for production and water flooding operations. Materials and methods: Advanced computational techniques, including machine learning applications, are explored for their role in improving evaluation accuracy. Additionally, this study compares different comparative analysis approaches used in the industry, highlighting their strengths, limitations, and adaptability to various geological conditions. Results: The synthesis of recent research demonstrates the potential of multimodal analysis approaches to enhance predictive accuracy and decision-making efficiency. Comparative evaluations reveal that while traditional methods remain valuable in certain contexts, data-driven techniques provide superior adaptability and scalability. Future advancements are identified in integrating real-time data streams and cross-disciplinary modeling. Conclusion: Data-driven comparative analysis, particularly when supported by machine learning, shows significant promise in improving reservoir management practices. By enabling more accurate drilling point selection and more effective water flooding operations, these approaches can drive both economic and operational efficiency. The study emphasizes the importance of continuous innovation and integration of computational tools to address the evolving complexity of reservoir systems.