The significance of comprehensive automation of the oil and gas complex for enhancing the rationality of nature management and reducing the aggregate ecological load on extraction and processing territories

Abstract

The article examines the impact of digital continuous monitoring systems for technological processes on reducing emergency hydrocarbon emissions and preventing soil and water body pollution in the oil and gas industry, using pipeline transport as an example. It is shown that traditional discrete control methods, relying on patrols and balance assessments, are characterized by significant inertia and fail to detect latent defect stages, thereby increasing the likelihood of cascading accidents. The analysis focuses on the architectures of modern software-hardware complexes (sensor levels, high-frequency telemetry, edge processing, analytical loops with hydrodynamic models and machine learning algorithms), as well as their ability to identify weak precursors of depressurization based on multidimensional indirect indicators (pressure fluctuations, acoustic and temperature anomalies) amid intense technological noise. Using telemetry and event log arrays from 2018-2023 for 142 confirmed incidents and 3800 false alarms, a comparison of leak detection system generations was performed in terms of detection time, sensitivity threshold, emission volume, and selectivity. The presented numerical dependencies demonstrate that the transition from parametric balance solutions to wave, combined neural network, and fiber-optic approaches reduces the average detection time from tens of minutes to fractions of a minute while simultaneously lowering the proportion of false alarms, thereby eliminating the operator's habituation effect to alerts. A nonlinear relationship between response time and pollution scale has been established: the volume of contaminated soil and reclamation complexity increase acceleratedly, transforming detection delay from a technical issue into a factor of multiple amplification of environmental and economic damage; in the structure of losses from delayed response, reclamation and fines dominate, while the cost of lost product becomes secondary. Special emphasis is placed on requirements for the reliability of information flows and cyber resilience of industrial systems, as connecting monitoring to network infrastructure introduces new risks of data tampering. The material enables evaluation of the relevance of monitoring digitalization as a key tool for proactive management of man-made and environmental risks and as a foundation for further prediction of equipment residual life based on accumulated big data.