EARLY DETECTION OF COMPLICATIONS OCCURRING DURING THE DRILLING OF OIL AND GAS WELLS

The article examines the issues of developing and implementing intelligent early warning systems for the proactive detection and prevention of technological complications arising during onshore and offshore oil and gas well drilling. Against the backdrop of substantial financial investments allocated to drilling operations, enhancing safety and efficiency is identified as the primary objective. By processing large volumes of real-time data received from geological and technological stations using neural networks and machine learning methods, hidden relationships are identified and anomalies are predicted. Within the scope of the research, an intelligent platform based on the integration of archival, real-time, and simulation data has been developed. The system is capable of predicting complications such as pipe sticking and drilling fluid losses 15–30 minutes in advance, enabling a significant reduction in non-productive time. Testing conducted using open data from the Volve field demonstrated that complications were detected with an accuracy of 89–96%. The results obtained indicate that the application of artificial intelligence–based warning systems is appropriate for improving the safety, speed, and overall economic efficiency of drilling processes. Keywords: intelligent warning system, machine learning, neural networks, geodata anomaly detection, non-productive time.