Abstract
Artificial intelligence is rapidly gaining ground in the oil and gas industry, driven by the need to improve the efficiency of reservoir development and streamline production operations. One of the most promising applications of AI is the analysis of data collected by downhole monitoring systems – particularly those designed to measure bottomhole pressure. As more permanent downhole gauges are deployed across the industry, operators now have access to continuous, real-time insight into reservoir pressure behavior. The widespread use of permanent downhole pressure gauges enables continuous, real-time data collection on reservoir pressure dynamics. As part of a broader big data environment, these data sets require modern architectures for storage, processing and analysis. By applying machine learning algorithms – such as neural networks and regression models – engineers can uncover hidden patterns, predict reservoir parameters, perform transient pressure analysis without shutting down wells, and improve real-time decision making in field operations. This paper reviews the design principles of pressure monitoring systems and examines modern big data architectures, including lambda, kappa and unified frameworks. It also highlights practical applications of machine learning algorithms using both field data and synthetic datasets. The paper demonstrates the effectiveness of combining proxy modelling with machine learning to assess inter-well connectivity and predict production behavior. The discussion is based on real-world case studies from international and Kazakh oil fields, including the use of CRMP-based digital solutions and ensemble modelling approaches.