Development of intelligent systems for automation of technological processes in the oil and gas industry based on machine learning and predictive analytics methods

Abstract

The development of intelligent systems for automating technological processes in oil and gas production relies on the synthesis of machine learning methods and predictive analytics in order to overcome the limitations of traditional deterministic models under conditions of increasing uncertainty regarding reservoir characteristics and non-stationarity of production. Continuous data streams from downhole sensors, flow meters, and vibration diagnostics, the volume of which reaches terabytes per day at a single field, are transformed into operationally significant predictions by means of ensemble gradient boosting algorithms, recurrent and convolutional neural networks, as well as transformer architectures and reinforcement learning. Hybrid models demonstrate high accuracy in the early detection of pre-failure states of electric submersible pumps, achieving an AUC-ROC value of 0.937, and in the prediction of liquid flow rate with a coefficient of determination of 0.931 and a mean absolute percentage error of 6,23%. Adaptive control of equipment operating modes based on Proximal Policy Optimization provides a reduction in specific electricity consumption by 18,3% while maintaining the production level and decreasing emergency incidents by 31,3%. A multi-level microservice architecture utilizing containerization, Kubernetes orchestration, and model lifecycle management tools ensures fault tolerance, low latency, and scalability to several thousand wells. Industrial tests confirm a reduction in non-productive time by 22,7%, growth in the fleet utilization coefficient, and an increase in production by 3,8%. Particular attention is paid to integration with legacy automation systems, ensuring the explainability of decisions through SHAP analysis, compliance with industrial safety requirements via independent protection circuits, and phased implementation that promotes the development of personnel trust. The presented approaches reveal the interdisciplinary character of the industry’s digital transformation by uniting software engineering, control theory, and applied mathematics, and demonstrate the potential for adaptation in adjacent energy sectors, which permits an assessment of the practical value of the full version of this work for specialists in industrial artificial intelligence.