Automation of accounting and disposal of drilling mud as a factor in reducing the load on landfills and improving the indicators of circular use of resources

Abstract

The article substantiates the automation of accounting and utilization of drilling waste as a key factor in shifting from a cost-intensive landfill disposal model to circular resource use and reducing landfill burdens; the aim is to quantitatively assess the technological, economic, and environmental effects of implementing an loT-oriented accounting system versus traditional manual methods. Based on an empirical dataset from 12 drilling sites in the Khanty-Mansi Autonomous Okrug-Yugra (2019-2023), a quasi-experiment was conducted with two modes: pre-implementation (visual-instrument measurements, logbook recording) and pilot-industrial operation of an automated system (ultrasonic and laser level sensors, conveyor scales, GLONASS/GPS, and a unified software platform with dispatch analytics). Methodologically, descriptive statistics, Student's t-test, analysis of variance, and correlation-regression analysis were used, alongside economic evaluation (comparison of specific and total costs, NPV, payback period) and comparative eco-logistics costing. The results showed that the share of landfilling was more than halved - from 93,9% (baseline) to 42.9% by 2023 - while the utilization share rose to 57,1%; specific utilization costs averaged 15,4% lower than landfilling (1,92 vs 2,27 thousand RUB/t), and total annual waste-management expenses decreased by 16.18%. Information accuracy improved: measurement discrepancies fell to 1,5-2% (versus about 25% previously), a strong negative relationship was identified between the utilization share and total costs (R=-0.98; p меньше 0.001), and a systematic underestimation of volumes (~1,4%) was corrected. The combined effect includes a 42,18% reduction in landfilled waste volumes, a 656,9% increase in secondary circulation involvement, a 42,22% reduction in required landfill area, and a decrease in the transport carbon footprint; the economic gain for the 12 sites was about 3.52 million RUB/year with a 2,5-3-year payback. The discussion underscores the role of accurate data flows for proactive logistics (Just-in-Time), targeted selection of utilization technologies, and establishing transparent contracts; limitations include variability in sludge composition and infrastructure scalability. Further integrations with ML are recommended for predictive composition analysis and dynamic route optimization.