Automation of Neural Network Forecasting of Emergency Situations in the Management Tasks of Oil and Gas Industry

This study addresses the automation of predicting emergency situations in control systems through artificial-intelligence methods. As a case example, well-operation control is examined: the prediction of emergencies is carried out by processing large volumes of data comprising sensor readings from the hoisting-unit telemetry system and routine event logs from the well. To handle these data sets, a recurrent neural network is proposed. A mathematical model is constructed, on the basis of which a structural model of the prototype prediction program integrating data and knowledge is developed. A conceptual architectural scheme of the neural-network-based program for automating emergency prediction in control systems is presented, together with an information model and a description of the program’s operating principles. The program is implemented in Python using the Pandas and PyTorch libraries. The paper reports the resulting performance metrics, which also enable related tasks such as linking emergency situations to specific crews and repair types. Neuralnetwork varieties suited to particular subtasks are discussed: recurrent neural networks (RNN), convolutional neural networks (CNN), and long short-term memory networks (LSTM). Developing a neural-network program for automated emergency prediction represents an important tool for improving safety, reducing risks, and optimizing production processes in the oiland gas industry.