Research Intelligent Methods and Machine Learning Algorithms for Identification Anomalies in the Drilling Operations

Intelligent methods can analyze large volumes of data coming from a digital twin and identify bottlenecks in technological processes. Digital twins provide the ability to conduct scenario analysis and forecast various situations within technological processes. The use of digital twins and algorithms based on machine learning and data analysis helps improve product quality by identifying anomalies and problems at early stages, thereby allowing you to promptly make the necessary changes, which significantly reduces the time and financial costs of implementing the final product. Intelligent decision support systems can provide recommendations based on data analysis, both retrospective and received in real time, which significantly simplifies solving complex problems and improves the quality of decisions made. Thus, the integration of intelligent decision support methods and algorithms with digital twins can significantly improve the productivity, competitiveness and innovation of oil and gas enterprises. As part of the study, we analyzed the most suitable machine learning methods and algorithms for data labeling aimed at identifying anomalies in the process of stirring.