Development of AI-driven Real-Time Reservoir Data Analysis System

Thesis Abstract

The increasing complexity and volume of reservoir data generated by modern extraction technologies have underscored the necessity for advanced analytical systems capable of real-time processing to optimize exploration and production activities in the petroleum industry. This study addresses the challenge of timely and accurate interpretation of large-scale reservoir data by developing an AI-driven real-time data analysis system, aimed at enhancing decision-making efficacy in reservoir management. The primary objective is to design, develop, and validate an intelligent platform that integrates machine learning algorithms with reservoir data streams to provide immediate insights into reservoir performance, pressure variations, and fluid flow dynamics. Secondary objectives include assessing the system's predictive accuracy, operational efficiency improvements, and its scalability across different reservoir contexts. Employing a mixed-method research design, the study combines quantitative data analysis with qualitative system validation. The quantitative component involves collecting reservoir data from 15 mature oil fields operated by a regional oil company over a 24-month period, resulting in a dataset comprising over 1 million data points, including pressure, temperature, and flow rate measurements. Data collection instruments include sensor networks, SCADA systems, and digital logs, which are synchronized and integrated within a centralized data repository. The AI system development follows an iterative process, utilizing supervised learning models such as Random Forest and Support Vector Machines (SVM) for predictive analytics, and unsupervised techniques like clustering for identifying reservoir anomalies. The system’s performance is evaluated through cross-validation procedures, with accuracy, precision, and recall metrics determining its efficacy in real-time prediction tasks. Qualitative validation entails expert reviews and interviews with reservoir engineers to ensure the system’s outputs align with operational knowledge and practical expectations. Data analysis encompasses the application of regression analysis and time-series forecasting to evaluate predictive accuracy, while ANOVA tests assess system performance variations across different reservoir types and operational conditions. The research also employs thematic analysis of expert feedback to refine system interface design and usability. Expected findings suggest that the AI-driven system can accurately predict reservoir pressure drops with over 92% accuracy within seconds of data acquisition, significantly reducing the latency associated with traditional data processing methods. Furthermore, the system demonstrates an ability to detect early signs of malfunction or reservoir heterogeneity, thereby facilitating proactive intervention. The integration of machine learning models enhances the predictive capabilities without requiring extensive manual input, demonstrating scalability and adaptability. This research contributes to existing knowledge by pioneering an integrated AI-based framework for continuous real-time reservoir data analysis, bridging the gap between high-volume data generation and actionable insights. It advances the application of machine learning in reservoir engineering, offering a scalable solution that can be extended across diverse geological settings and operational scales. The study also provides a methodological blueprint for deploying AI systems in other complex subsurface data environments. The main conclusion affirms that AI-driven real-time analysis systems materially improve reservoir management by enhancing predictive accuracy, operational responsiveness, and decision-making agility. Recommendations include broader deployment of the system across different reservoir contexts, ongoing integration of emerging machine learning techniques for improved accuracy, and continuous user training to maximize system utility. Future research should focus on incorporating multi-physics models into the AI framework and exploring their synergistic effects on reservoir performance predictions. This study ultimately underscores the transformative potential of artificial intelligence in revolutionizing data-driven petroleum engineering practices.