The role of SCADA systems in optimizing field production under environmental constraints

Abstract

The article examines the role of modern SCADA systems as the core of digital production optimization at oil and gas fields amid tightening environmental requirements, aiming to quantitatively assess production, energy, and environmental effects from transitioning from local dispatching to a unified integrated platform with predictive analytics. The empirical base covers 12 fields over 7 years, comparing pre- and post-implementation periods: more than 50 TB of telemetry from 25,000+ sensors across wells, booster pump stations, oil treatment units, compressor assets, and power facilities were processed; data cleansing, correlation and regression analysis, Student’s t-test, time-series analysis, and ML algorithms (gradient boosting) were applied for predictive maintenance and operating mode optimization. The results show a 6,4% increase in average daily production alongside a 6,74% rise in well uptime and a 37,16% reduction in downtime, which lowered unit operating costs by 13,39% by shifting from a run-to-failure model to data-driven, proactive operations. Environmental effects include an 18,21% reduction in GHG emissions, a 63,19% decrease in fugitive methane leaks, a 68,72% reduction in flared associated petroleum gas, and a 72,22% drop in spill counts thanks to continuous monitoring, gas-flow balancing, and automatic isolating control. Energy efficiency improved: specific energy consumption per tonne of production -14,6%, artificial lift efficiency +20%, and network losses -29,35% due to SCADA integration with variable speed drives and network configuration optimization. Reliability increased: MTBF +147,06%, MTTR -62,35%, and critical shutdowns -72,00%, with a strong correlation between higher MTBF and fewer environmental incidents, and an inverse relationship between specific energy and OPEX. The conclusions confirm SCADA’s transformation from passive visualization to an intelligent platform for multicriteria optimization, and the next steps are integration with AI and digital twins for autonomous control scenarios and deeper eco-economic optimization.