A Conceptual Framework for Automating Reservoir Characterization Workflows Using NLP and Data Integration

Reservoir characterization is a critical process in the energy industry, providing insights into subsurface properties essential for efficient exploration, production, and management. Traditional workflows are hindered by inefficiencies in handling diverse datasets, reliance on manual processes, and the challenges of integrating structured and unstructured information. This paper proposes a conceptual framework to automate reservoir characterization workflows by leveraging advanced technologies such as Natural Language Processing (NLP), data integration tools, and visualization systems. The framework encompasses four key components: data acquisition and preprocessing, text analysis through NLP models, a robust integration layer for harmonizing datasets, and intuitive visualization tools for enhanced decision-making. The framework aims to improve efficiency, accuracy, and collaboration in reservoir studies by automating and streamlining these processes. The paper also discusses the anticipated benefits, including faster decision-making and greater insight, alongside challenges such as computational demands, data quality issues, and the need for domain-specific model customization. Strategies to mitigate these challenges and recommendations for future research and practical implementation are proposed. This framework represents a transformative approach, offering a scalable solution to modernize reservoir characterization and drive innovation in the energy sector.