Robot Field Development Teams: Harnessing Multi-Agent Artificial Intelligence Systems in Petroleum Engineering

摘要

Abstract This paper explores the transformative potential of multi-agent artificial intelligence (AI), highlighting their distinctive capabilities in collaborative decision-making, dynamic adaptability, and the seamless integration of diverse domain expertise within petroleum engineering. Multi-agent AI systems, especially when combined with advanced large language models (LLMs), are gaining traction across various industries including healthcare, finance, logistics, and cybersecurity due to their capacity for complex reasoning, interactive communication, and real-time data processing. Demonstrating their robust applicability, this paper presents three practical case studies within petroleum engineering: oilfield data summarization, automated pressure-volume-temperature (PVT) table correction, and machine learning-based rock type classification from wireline log data. In the first case study, a multi-agent system is deployed to emulate a multidisciplinary team comprising domain-specific agents—including geologists, geophysicists, petrophysicists, reservoir engineers, and production technologists. These agents collaboratively synthesize extensive oilfield data into structured summaries, drastically reducing manual processing time and enhancing the accuracy of insights. The second study illustrates how a specialized multi-agent crew effectively automates the analysis and correction of black oil PVT data. By extracting, validating, and adjusting PVT datasets to separator conditions, the system improves the reliability of fluid-property data crucial for reservoir modeling, substantially minimizing human-induced errors. The third case study addresses automated rock classification by leveraging data analytics and neural network modeling. Agents systematically analyze and interpret wireline log data, accurately classifying rock types into lithofacies. This approach significantly improves interpretative consistency and analytical efficiency compared to traditional manual methods. Collectively, these studies demonstrate that multi-agent AI systems not only replicate complex petroleum engineering workflows but enhance them by automating repetitive tasks, reducing errors, and providing high-quality decision support. The implementation of multi-agent systems thus represents a robust step forward in the digital transformation of oil and gas field development, fostering greater operational agility, resilience, and innovation.