The article reflects the basic principles of the application of artificial intelligence (AI) and machine learning (ML) technologies at oil refineries, with a particular focus on Russian industrial enterprises. Modern oil refineries are equipped with numerous sensors embedded in technological units, generating vast volumes of heterogeneous data in real time. Effective processing of this data is essential not only for maintaining the stable operation of equipment but also for optimizing energy consumption, which is especially relevant under the increasing global demand for energy resources. The study highlights how AI and ML methods are transforming data management in the oil industry by enabling predictive analytics and real-time decision-making. Python programming language plays a central role in this process due to its open-source ecosystem, flexibility, and extensive set of specialized libraries. Key libraries are categorized and discussed: for data preprocessing and manipulation (NumPy, SciPy, Pandas, Dask), for visualization (Matplotlib, Seaborn, Plotly), and for building predictive models (Scikit-learn, PyTorch, TensorFlow, Keras, Statsmodels). In addition, the article discusses the importance of model validation, hyperparameter tuning, and the automation of ML workflows using pipelines to improve the accuracy and adaptability of predictions under variable operating conditions. Through practical examples based on real industrial datasets, the authors demonstrate the capabilities of Python tools in creating interpretable and robust AI solutions that help improve energy efficiency and support digital transformation in the oil refining sector.

Keywords: machine learning (ML), artificial intelligence (AI), intelligent data analysis, Python, Scikit-learn, forecasting, energy consumption, oil refining, oil and gas industry, oil refinery

The article considers the issues of risk management in the implementation of investment construction projects in the oil and gas industry. The author's classification of risks, methods for assessing project risks are presented and directions for reducing risks in the implementation of construction projects in the oil and gas industry are proposed. The author attempts to determine the importance of the risk of uncertainty in the implementation of oil and gas projects in the context of instability of economic and political factors.

Keywords: risk assessment, risk of uncertainty, project management, oil and gas industry, investment construction project

The article considers the sequence and technology of conducting surveying before starting excavation work on a bush site for the oil and gas industry. Surveying works play a key role, ensuring the necessary accuracy and reliability of the development of oil and gas fields. These works include conducting field measurements, detailed terrain analysis, calculating the volume of soil masses involved in the construction of earthworks, as well as monitoring geometric parameters and sand pits. An important stage of surveying is the creation of a detailed topographic map of the territory. Based on this map, it is possible to accurately plan the location of all future structures, ensuring optimal use of space and compliance with all regulatory requirements. The use of modern technologies, such as geodetic GPS systems and BIM technology, can significantly improve the accuracy and efficiency of these works. Special attention is paid to compliance with industrial safety standards, which minimizes costs and risks in the development of oil and gas industry enterprises. Thus, surveying is an integral part of the preparation and implementation of projects in the oil and gas field, contributing to the efficient and safe performance of all necessary work.

Keywords: surveying, engineering geodesy, earthworks, development of oil and gas fields, bush site, executive survey

A comprehensive methodology for calculating assessment of wells reliability and condition in terms of submersible equipment operation, operating time and additional negative factors of operation through Boolean and fuzzy logic has been developed. The methodology includes retrospective analysis, calculation for the current moment and calculation of future possible failures with the preparation of various option of wells repair schedule and their distribution into risk zones.

Keywords: boolean logic, fuzzy logic, time to failure, bath function, weighting factor, membership function, screening method