An AI-Driven Solution to Support Geothermal Drilling Operations and the Detection of Dysfunction in Real-Time at the Edge and Cloud

Abstract The exploration and exploitation of geothermal energy resources in Europe present unique drilling challenges that necessitate innovative solutions for efficient and sustainable operations. One such innovation is the application of an AI-driven drilling solution originally developed for the oil and gas (O&G) sector. This paper discusses the adaptation and implementation of this technology to monitor and mitigate drilling dysfunctions in geothermal wells, particularly focusing on its effectiveness in detecting abnormal pressure losses. Geothermal drilling shares many similarities with O&G drilling, including the need to manage high temperatures, variable pressures, and complex geological formations. An AI-driven drilling solution provides a sophisticated approach to these challenges by integrating data analytics and machine learning to enhance real-time decision-making and operational efficiency. The AI system leverages extensive knowledge, data and experience from historical O&G drilling operations and engineers, applying advanced algorithms to predict drilling conditions and detect anomalies. One of the critical functionalities of this system along with drilling dysfunction detection, is its ability to identify abnormal pressure losses, which can indicate issues such as formation damage, fluid loss, or wellbore instability. By continuously analysing sensor data from the drilling operations, the AI solution can predict the onset of these conditions, providing operators with early warnings and recommendations for corrective actions.