Development of integrated process control systems in the oil and gas industry based on big data

Abstract

The development of the oil and gas complex requires the creation of effective process management tools in the context of the growing complexity of production facilities. The introduction of big data opens up new opportunities for improving the accuracy and efficiency of management by processing large amounts of information in real time. The purpose of this work is to develop an integrated process management system based on the analysis and use of methods of working with big data. The paper uses an approach based on the use of machine learning technologies, artificial intelligence and big data analytics. Methods of data collection, processing and analysis from various stages of the oil and gas complex production cycle were investigated. The IIoT (Internet of Things for Industry) architecture was used to build control systems in combination with cloud computing platforms and predictive analytics tools. A prototype process management system has been created, which includes modules for automating data collection, implementing predictive analysis algorithms, and visualizing information for making operational decisions. The proposed approach has improved key performance indicators, including reducing emergency response time by up to 30% and increasing planning efficiency by up to 25%. The possibility of integrating the system with existing automated control platforms is also demonstrated. The work has shown the high potential of using big data to optimize technological processes in the oil and gas industry. The research results can be applied to increase productivity, reduce production risks and costs, as well as to ensure the sustainable development of the complex. To further improve the proposed approach, it is planned to conduct additional tests on real objects and develop models for new areas, such as forecasting environmental risks.