MODERN CONCEPTUAL FOUNDATIONS FOR THE IMPLEMENTATION OF THE METHODOLOGY AND STRATEGY OF INTELLIGENT DECISION SUPPORT IN THE DRILLING PROCESS OF OIL AND GAS WELLS IN UKRAINE

Abstract

The article examines the transformation of drilling operations in the context of Industry 4.0 through the implementation of cloud computing, edge computing, and intelligent analytical solutions. The relevance of the study is determined by the need for rapid decision-making during drilling operations, which are characterized by complex technological conditions, a large number of telemetry parameters, nonlinear dependencies, and a high level of risk. It is shown that traditional SCADA/ICS systems are limited in their ability to perform in-depth real-time analysis and do not support adaptive predictive models required for modern cyber-physical drilling control systems.

In response to these challenges, the concept of an Intelligent Decision Support System (IDSS) is proposed, which integrates cloud architecture, machine learning tools, and modern telemetry access interfaces. The main focus is on creating an environment that ensures scalability, resilience to abnormal situations, and flexibility in deployment on edge or cloud platforms. The developed system integrates edge and cloud levels to implement a closed loop of “data collection – analysis – recommendation – control,” which is a fundamental feature of intelligent cyber-physical systems.

The article emphasizes the role of containerized services based on FastAPI, OPC UA connectors, and XGBoost algorithms in ensuring predictable behavior, high performance, and adaptability when processing large volumes of telemetry data. This approach enables the creation of dynamic predictive models of drilling parameters, including rate of penetration, facilitates anomaly detection, and allows timely generation of recommendations for operators.

The proposed architecture forms the technological foundation for the transition from reactive to predictive-analytical drilling control. It ensures rapid response to changing process parameters, flexible scalability of computational resources, and improved energy efficiency and operational safety. The obtained results demonstrate that the IDSS can serve as a foundation for next-generation DSS platforms aimed at intelligent, adaptive, and efficient drilling management in the era of Industry 4.0.