Development of AI-based Real-Time Drilling Parameter Monitoring System

Thesis Abstract

Effective monitoring of drilling parameters is critical for optimizing wellbore stability, reducing operational costs, and preventing drilling accidents in the petroleum industry. Traditional methods often rely on manual data interpretation and static monitoring systems, which can lead to delayed responses and suboptimal decision-making, especially in the face of rapidly fluctuating drilling conditions. This study aims to develop an AI-based real-time drilling parameter monitoring system that leverages advanced machine learning algorithms to enhance accuracy, responsiveness, and predictive capabilities in drilling operations. The specific objectives are to identify key drilling parameters that influence operational safety and efficiency, design and train machine learning models for real-time data analysis, evaluate the system’s predictive performance, and assess its usability and integration potential within existing drilling control frameworks. The research adopts a mixed-methods approach, combining quantitative data analysis with qualitative usability assessment. The quantitative component involves collecting drilling data from a sample of 50 well drilling sites over a period of 12 months, encompassing parameters such as rate of penetration, mud weight, rotary torque, standpipe pressure, and downhole temperature. Data are collected via automated sensors and centralized data acquisition systems, supplemented with operational logs. The qualitative aspect involves semi-structured interviews with 20 drilling engineers to evaluate system usability and integration challenges. Machine learning models, including regression algorithms, support vector machines, and neural networks, are trained and validated using cross-validation techniques to identify patterns and predict anomalies in real time. Statistical evaluation employs metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic curves to determine model performance. It is hypothesized that the AI-based monitoring system will significantly outperform traditional threshold-based systems in detecting drilling anomalies and predicting adverse events. The anticipated findings suggest improvements in early anomaly detection accuracy by at least 25%, reduction in false alarms, and increased operational efficiency through faster decision-making processes. The system is expected to demonstrate high predictive reliability, with an F1-score exceeding 0.85 across different drilling scenarios, and to be deemed user-friendly and adaptable by drilling personnel. This research contributes to knowledge by integrating machine learning techniques within the complex environment of drilling operations, thus advancing intelligent decision support systems in petroleum engineering. The study offers a novel framework that can be tailored to diverse operational contexts, emphasizing the role of artificial intelligence in enhancing real-time decision-making, safety, and productivity in upstream operations. Additionally, it bridges the gap identified in previous literature, which primarily focused on offline analysis, by providing a scalable and operationally viable solution for live monitoring. The main conclusions are that AI-driven monitoring systems substantially enhance real-time operational awareness and predictive accuracy in drilling. Recommendations include adopting the developed system across multiple drilling fleets, further refining machine learning models with expanded datasets, and integrating the system with existing drilling control infrastructure. Future research should explore the application of deep learning architectures and the potential of integrating sensor data with geomechanical or reservoir models to further enhance predictive capabilities. Overall, this study underscores the transformative potential of artificial intelligence in petroleum drilling, paving the way for safer, more efficient, and cost-effective operations.