December 26, 2025

Cognitive technologies and neural networks article

The article “Cognitive Technologies and Artificial Neural Networks in Environmental Monitoring Systems of the Oil and Gas Industry” authored by A. Nurgaliev and A. Nazarova has been published in Neftegaz (2025).

This work focuses on how neural-network architectures and cognitive technologies can transform environmental monitoring from basic data collection into intelligent, predictive emission management. The authors systematize a wide range of ANN architectures — including LSTM and GRU networks for time-series forecasting, convolutional networks for image and satellite-data analysis, autoencoders for anomaly detection and data recovery, as well as hybrid models and digital twins — and assess their applicability to monitoring methane and VOC emissions. The article links these technological developments with international MRV, ESG and OGMP 2.0 standards and with Russian programs such as “Clean Air” and “Smart Environmental Monitoring”, arguing that ANN-based systems are becoming a core element of future MRV platforms. Special attention is paid to real-time anomaly detection, self-calibration of sensor networks and the role of explainable AI in industrial safety and regulatory reporting. 

December 22, 2025

Data quality and ML reliability article

The article “Analysis of the Impact of Data Quality on the Reliability of a Machine Learning Algorithm for Predicting Casing Integrity Failures” by A. Karsakov, A. Nurgaliev and I. Sharf has been published in the journal Prospects of Science (2025).

The study addresses a practical question for digital diagnostics in the oil and gas industry: how much does the quality of field data affect the reliability of machine-learning models that predict well-casing integrity failures? Using a simulation model structurally equivalent to logistic regression, validated on synthetic data derived from real information on 79 wells, the authors test several scenarios with different levels of completeness and noise. The results show that when data quality drops from 0.95 to 0.75, forecast accuracy decreases by about 21 %, clearly illustrating the vulnerability of ML models to missing values and measurement errors. The article identifies minimum thresholds for stable use of ML in field monitoring — data completeness of at least 70 % and measurement error not exceeding 10 % — and argues that improving data-collection practices is a prerequisite for trustworthy AI-based diagnostics in well integrity management. 

November 26, 2025

AI-driven methane monitoring article

The article “AI, Low-Cost Sensors, and the New Era of Methane Compliance” by A. Nurgaliev has been published in Shale Magazine.

This article explores how advanced artificial-intelligence models combined with low-cost sensor networks are transforming methane-emissions compliance in the oil and gas sector. The study highlights how recent regulatory pressures — from enhanced EPA requirements to the Methane Rule — are accelerating the adoption of digital monitoring tools across upstream operations.

The author shows that AI-based analysis enables operators to detect small-scale leaks earlier, reduce operational risks, and significantly lower the cost of continuous monitoring compared to traditional LDAR programs.

The article emphasizes that the integration of neural networks, atmospheric data, and scalable sensor platforms creates a new technological foundation for proactive leak management and supports the transition toward cleaner and more transparent industrial practices.

>> Official source

November 26, 2025

AI Time Journal

The article “How AI Is Transforming Greenhouse-Gas Monitoring into a Predictive Industrial Risk-Management System” by A. Nurgaliev has been published in AI Time Journal.

This publication analyzes how artificial intelligence shifts greenhouse-gas monitoring from traditional measurement toward a fully predictive risk-management approach. The author examines the combined use of machine-learning models, real-time sensor networks, meteorological input, and industrial data to forecast emission anomalies before they escalate into safety or environmental incidents.

The article shows that AI-driven monitoring helps companies move beyond reactive reporting, enabling earlier hazard identification, reduction of methane and CO₂ intensity, and optimization of maintenance schedules.

It also demonstrates how predictive analytics can enhance ESG transparency, improve regulatory readiness, and support companies in deploying digital solutions that lower operational risks across oil, gas, and industrial facilities.

>> Official source

November 14, 2025

Carbon monitoring market article

The article “Carbon Monitoring Market: Instruments and Prospects” by A. Nurgaliev, A. Nazarova and I. Sharf has been published in Vectors of Well-Being: Economics and Society (2025, vol. 53, no. 4).

This study analyzes how the carbon-monitoring market is forming under the combined pressure of international climate agreements, national legislation and investor demand for ESG transparency. The authors examine institutional, market and technical instruments — from mandatory greenhouse-gas reporting and climate projects to carbon units and monitoring technologies — and show that the market is still at an early stage but has significant growth potential. The article explains how carbon-monitoring tools stimulate companies to move toward carbon neutrality, increase their investment attractiveness and, at the same time, require the development of domestic technical solutions and software tailored to the specific needs of the oil and gas industry.

DOI: 10.18799/26584956/2025/4/2061 

October 14, 2025

Technologies of environmental emissions monitoring

The article “Environmental emissions monitoring technologies in the oil and gas industry: comparative analysis and integration perspectives” by A. Nurgaliev, A. Nazarova, A. Shadrina, O. Brusnik and M. Momeni has been published in the Russian industry journal Neftegaz (2025).

The paper reviews a wide spectrum of technologies used to monitor methane, CO₂ and volatile organic compound emissions in the oil and gas sector. Against the background of tightening requirements such as EU Regulation 2024/1787, the U.S. EPA Methane Rule and OGMP 2.0, the authors compare IoT/IIoT sensor networks, digital twins, satellite and airborne methods, and high-precision spectroscopy. They demonstrate that no single technology is universal: each has its own strengths, limitations and cost profile. The article concludes that the highest effectiveness is achieved when these tools are integrated into a unified architecture that combines global coverage from satellites, local sensitivity of ground-based sensor networks, predictive capabilities of digital twins and reference-grade measurements. Such hybrid systems, according to the authors, are key to meeting both climate-policy targets and practical operational needs of oil and gas companies. 

>> Official source

October 1, 2025

Article on AI and environmental monitoring

The article “Issues of improving environmental monitoring at oil and gas facilities using artificial intelligence technologies” authored by A. Nurgaliev, A. Nazarova and I. Sharf has been published in Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering (2025, vol. 336, no. 10, pp. 38–49).

The paper examines how traditional environmental monitoring systems in the oil and gas sector struggle to keep pace with climate and ESG requirements. The authors show that current approaches are limited by three key constraints: the quality and completeness of monitoring data, the availability of measuring devices suitable for harsh climatic conditions, and the maturity of AI technologies that can actually be deployed at industrial sites. The article argues that integrating artificial intelligence into air-quality monitoring can shift the focus from delayed, fragmented measurements to continuous, data-driven control of greenhouse-gas emissions. It also demonstrates that the development of in-house software platforms, although technically challenging, can become a scalable business solution for companies operating in regions with similar geographic and climatic conditions.

DOI: 10.18799/24131830/2025/10/5289 

>> Official source