Impact of digital systems for monitoring technological processes on reducing emergency emissions and preventing soil and water body pollution in the oil and gas industry

Abstract

The article examines the impact of digital continuous monitoring systems for technological processes on reducing accidental hydrocarbon emissions and preventing soil and water body contamination 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 a dataset of telemetry and event logs from 2018–2023 covering 142 confirmed incidents and 3800 false alarms, a comparison of leak detection system generations was conducted in terms of detection time, sensitivity threshold, emission volume, and selectivity. The presented numerical dependencies demonstrate that transitioning from parametric balance solutions to wave-based, 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 false positive rate, thereby eliminating the operator habituation effect to alarms. A nonlinear relationship between response time and contamination scale has been established: the volume of contaminated soil and reclamation complexity grow exponentially, transforming detection delays from a technical issue into a multiplier of environmental and economic damage; in the loss structure from delayed responses, reclamation and penalty sanctions dominate, with the cost of lost product becoming secondary. Special emphasis is placed on requirements for information flow reliability and cyber resilience of industrial systems, as integrating monitoring into network infrastructure introduces new data spoofing risks. 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.