Gas Industry Research Articles

Deep purification of perfluorinated electronic specialty gas with a scalable metal-organic framework featuring tailored positive potential traps

The sequestration of trace hexafluoropropylene (C3F6) is a critical yet formidable task in the production of high-purity perfluoropropane (C3F8), an important perfluorinated electronic specialty gas (F-gas) in the advanced electronics industry. Traditional adsorbents struggle with uneven, low-pressure uptake and compromises in selectivity. This work utilizes aperture size-electrostatic potential matching within a robust metal-organic framework (Al-PMA) to facilitate selective, reversible binding of C3F6 while excluding larger C3F8 molecules. The presence of bridging hydroxyl groups (μ2-OH) in Al-PMA creates positive electrostatic potential traps that securely anchor C3F6 through strong hydrogen bonding, evidenced by in-situ infrared and 19F magic angle spinning nuclear magnetic resonance spectroscopy. Breakthrough experiments demonstrate the efficient removal of trace C3F6 from C3F8 under ambient conditions, achieving C3F8 purity exceeding 99.999%. The scalability of Al-PMA synthesis, remarkable stability, and exceptional performance highlight its potential as a promising adsorbent for industrial C3F6/C3F8 separations.

Effects of industrial hydrogen-rich gases on the performance of the spark ignition engine under various combustion modes

Hydrogen is emerging as a promising alternative to traditional fossil fuels for the transportation. Additionally, the hydrogen-rich gases (HRG) can produce on a large scale during the process of chemical production. In this study, the influences of HRG on the performance behaviors of the spark ignition (SI) engine are comprehensively analyzed under various combustion modes and operating conditions. The experimental results indicate that with increasing the lambda, the torque of the test engine can maintain the target value under equivalent ratio and lean combustion modes, while it sharply decreases under ultra-lean combustion. When the value of the lambda is 1.5, the brake thermal efficiency (BTE) and brake specific hydrogen consumption (BSHC) of the HRG SI engine respectively reach up to 33.6% and 89.1 g/kW.h, yielding superior fuel economy. The NOx emissions of the HRG SI engine firstly increase and then decrease with increasing lambda, reaching the peak value at the lambda of 1.1. In addition, the NOx, CO, and HC emissions of the HRG SI engine greatly reduce when the lambda is approximately 1.5. Consequently, using HRG partially substitute the traditional fossil gasoline in the SI engine can benefit to achieve energy conservation and emissions reduction in transportation section.

Optimization of carbon dioxide adsorption from industrial flue gas using zeolite 13X: A simulation study with Aspen Adsorption and response surface methodology

One of the best ways to prevent further warming of the planet and reduce pollutants is to remove or adsorb and recover carbon dioxide gas from the exit of cement factory exhaust. So far, several methods have been proposed for this; In this research, zeolite 13X adsorbent is used considering the operational conditions. To remove CO2 gas from the desired gas mixture, an adsorption operation unit with a column filled with zeolite 13X adsorbent was simulated in Aspen Adsorption V14 software. Changes in parameters such as temperature, and pressure and their effects on adsorption rate, adsorbent saturation time, and output heat rate were investigated to achieve higher system efficiency and optimal operating conditions. The adsorption unit was optimized by RSM to achieve maximum CO2 adsorption. Temperature and mole fraction of CO2 were identified as key influencing factors determining the adsorption performance of zeolite 13X for CO2 adsorption. It was found that the system should be operated at a temperature of 28.07 °C and a pressure of 1.202 bar to achieve maximum adsorption. Finally, 2.784 mol% CO2 gas was captured by the said process in optimal operating conditions. Further analysis revealed higher temperatures slightly decrease CO2 adsorption capacity, while increasing pressure enhances it. Conversely, elevated flow rates reduce breakthrough time, indicating a trade-off between adsorption capacity and capture efficiency.

Catalytic Co-oxidation of Dichloromethane and non-chlorinated VOC over HxPO4-RuOx/CeO2, Pt/HZSM-5 and dry-mixed Ru-Pt Catalysts

Industrial flue gases contain chlorinated VOCs (CVOCs) and hydrocarbons, along with various contaminants. Although previous studies have primarily focused on the catalytic oxidation of individual VOCs, the activity of VOCs in mixtures often differs from that of individual compounds. The catalytic oxidation of dichloromethane in the presence of a second VOC (ethyl acetate, n-hexane, and toluene) was investigated using HxPO4-RuOx/CeO2, Pt/HZSM-5, and dry-mixed Pt-Ru catalysts. The dry-mixed catalyst combines the excellent dechlorination ability of HxPO4-RuOx/CeO2 with the oxidation capability of Pt/HZSM-5. A mixture of 1000 ppm toluene and 100 ppm dichloromethane is fully oxidized over the Ru-Pt catalyst at 300 °C, 30 °C lower than the T100 observed on the other two catalysts. DFT calculation reveals that the activation of toluene and dichloromethane occurs on Pt and Ru sites, respectively, dechlorinated by proton of phosphate. Furthermore, by-product dioxin content on Pt-Ru is significantly below 0.1ng TEQ Nm-3 limit.

Pore-scale study of [formula omitted] desublimation in a contact liquid

Cryogenic carbon capture (CCC) designed to operate in a contact liquid is an innovative technology for capturing CO2 from industrial flue gases, helping mitigate climate change. Understanding CO2 desublimation properties in a contact liquid is crucial to optimizing CCC, but is challenging due to the complex physics involved. In this work, a multiphysics lattice Boltzmann (LB) model is developed to investigate CO2 desublimation in a contact liquid for various operating conditions, with the multiple and fully-coupled physics being incorporated (i.e., two-phase flow, heat transfer across three phases, CO2 transport between the gas and liquid, homogeneous and heterogeneous desublimation of CO2, and solid CO2 generation). The CO2 desublimation process in a contact liquid is well reproduced. Moreover, parametric studies and quantitative analyses are set out to identify optimal conditions for CCC. The decreasing liquid temperature (Tl) and flue gas temperature (T0) are found to accelerate the CO2 desublimation rate and enhance the CO2 capture velocity (vc). However, excessively low Tl and T0 values should be avoided. These conditions increase the energy consumption of cooling while only marginally improving vc, due to the limited CO2 supply. The CCC system performs effectively when purifying flue gases with high CO2 content (Y0). This is because the large Y0 accelerates the CO2 desublimation rate and enhances the overall CO2 capture efficiency. A high gas injection velocity (or Pe) is beneficial for amplifying vc by increasing the gas–liquid interfaces and enhancing the CO2 supply. Nevertheless, too high a Pe should be avoided, as it hinders the transport of CO2 to the liquid or solid CO2 surfaces, ultimately restricting the amount of CO2 available for desublimation and inhibiting the enhancement of vc. This study develops a viable LB model to investigate CO2 desublimation in a contact liquid for varying conditions, which advances the knowledge base of CCC and facilitates its industrial applications.

Mitigating China’s process-related greenhouse gas emissions via supply chain management

As the world’s largest industrial producer, China’s deep decarbonization of industrial process-related greenhouse gas (GHG) emissions is essential in fulfilling the goal of reaching net zero emissions. Previous studies have mainly focused on emission inventories and abatement technologies, however, final demand may also be the main driver of industrial process-related GHG emissions in China. Based on environmentally extended input-output analysis and structural path analysis, this study explores the detailed industrial process-related GHG emissions inventory and investigates the detailed supply chain paths of 17 products and 16 types of GHG emissions. The results show that industrial process-related GHG emissions reached 1887.1 Mt CO2-eq in 2018, of which more than three-fifths was induced by fixed capital formation. The entirety of the Construction sector amounted to 54.6% of the total embodied industrial process-related GHG emissions. After examining the embodied industrial process-related GHG emissions paths, critical economic sectors such as Construction, Nonmetallic Mineral, Chemical, and the paths of “Nonmetallic Mineral → Construction → Fixed capital formation and Nonmetallic Mineral → Nonmetallic Mineral → Construction → Fixed capital formation” were identified as the main contributor. In considering the enormous challenges of mitigating GHG emissions from process-related industries, it is essential to identify and optimize critical supply chains, which provides new insights into China's transition towards a low-carbon economy within its industrial processes.

Novel synthesis and application of zeolite-Y from waste clay for efficient CO2 capture and water purification

In the present study, the microwave-assisted hydrothermal method using sodium hydroxide (NaOH) has been used to synthesise zeolite-Y from three different waste clays (WC) from Teesside, Northeast of England, UK. The effects of microwave time intervals and WC type were investigated to produce crystalline zeolite-Y. All WCs and synthesised zeolites were characterised by scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and Brunauer–Emmett–Teller surface area measurement (BET). The characterisation results showed that the zeolite-Y samples synthesised at 6 min intervals exhibited larger total pore volumes, higher surface areas, and higher crystallinity compared to the samples synthesised at 4 min intervals. Especially, the synthesised zeolite-Y from Clay 1 displayed high CO2 adsorption capacity, achieving 5.23 mmol/g at 298 K and 1 bar, with a maximum methylene blue removal of 94.3 %. This sample showed a surface area of 485 m²/g and a pore volume of 0.24 cm³/g, alongside an Si/Al ratio of 3.7, highlighting its excellent thermal stability at elevated temperatures. The high CO2 adsorption capacity combined with the high methylene blue removal efficacy makes the synthesised zeolite-Y from WC an excellent candidate for efficient CO2 capture and removal of methylene blue from contaminated water. Additionally, this study illustrates the potential for waste valorisation through the conversion of low-value WC into high-performance, value-added materials. This transformation not only mitigates waste disposal issues but also contributes to sustainable resource management and environmental protection by offering a practical solution for treating industrial effluents and gas emissions.

Enhancing the performance of biofilters with sorption ability for simultaneous purification of landfill gases using laterite soil-based substrate

Various harmful gas emissions are a major controversial issue globally, and open dumpsites have become a leading contributor to atmospheric pollution. The study aims to introduce a high-performance landfill gas purification substrate using laterite soil, an iron, and aluminum-rich, highly available soil type in tropical countries; the final suggested substrate contained uniformly mixed laterite soil and compost with a 4:1 fraction by volume. A self-design setup that can maintain moisture content and aerobic conditions was used with an active landfill gas collection system through the gas wells established on the Karadiyana landfill in Sri Lanka under a tropical climate. The elimination capacity of CH4, NH3, VOCs, and H2S were 202860 ± 51595, 91 ± 30, 75 ± 27, and 6 ± 3 mg m−3 h−1 with 91 ± 3, 96 ± 1, 93 ± 3 and 83 ± 4% of removal efficiencies respectively at a 9.4 min empty bed residence time (EBRT). Selected laterite soil acts as an adsorption material and provides a better bio-filtering substrate with compost due to its specific characteristics. The hybrid gas purification process, absorption, and biofiltration within the proposed material were the reasons for higher performance, and the setup may be successful under the industrial waste gas streams. Further investigation should be carried out to study the medium's effectiveness as the covering layer on the dump with the successful lowest thickness, regarding secondary pollutants, and industrial applicability under unique specific conditions.

Fixed-bed natural gas CO2 adsorption performance of ZSM-5 zeolites impregnated with amines

This research studies the fixed-bed natural gas carbon dioxide (CO2) adsorption performance of ZSM-5 zeolites impregnation with different amines: monoethanolamine (MEA), ethylenediamine (ED), ethylenediaminetetraacetic acid (EDTA), and 4-aminophenol. The structural, morphological, and performance characteristics of the modified zeolites were examined through the use of several analytical techniques. These techniques featured Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The thermogravimetric analysis revealed weight losses in two stages linked to the removal of bound water molecules and the breakdown of the hydroxyl structure. Through fixed bed adsorption experiments, it was discovered that ZSM-5 zeolites functionalized with MEA/ED showed the CO2 adsorption capacity. This study investigated how well four different mathematical models—Clark, Yoon-Nelson, Thomas, and Adams-Bohart—could predict CO2 adsorption breakthrough curves from natural gas using non-linear regression methods. The results showed that all models had very high adjusted determination coefficients (Adj. R²) of over 0.99, indicating they matched the experimental data really well for various adsorbents. The analysis highlighted the models' effectiveness in reflecting adsorption kinetics and capacities, particularly noting the superior performance of the Thomas, Adams-Bohart, and Yoon-Nelson models in capturing uniform breakthrough behaviors. These findings underscore the models' utility in enhancing CO2 removal processes in natural gas purification, which is pivotal for optimizing industrial gas processing systems. These results highlight the efficiency of amine-functionalized ZSM 5 zeolites in capturing CO2 from natural gas streams.

Non-sintered ceramsite from alkali-activated gasification slag for adsorption through the regulation of physical properties and pore structure

With the development of the coal gasification industry, the comprehensive utilization of coal gasification fine slag (CGFS) has become increasingly important. This study explored the potential of producing non-sintered ceramsite by combining CGFS with blast furnace slag (BFS). The impacts of the alkali activator modulus, dosage, and BFS-CGFS ratio on the ceramsite's cylinder compressive strength, bulk density, apparent density, water adsorption ratio, hydration products, microstructure, and adsorption capacity were investigated. Moreover, the leaching behavior of heavy metals in acidic environments and the production costs were evaluated. The results indicated that the modulus and dosage of the alkali activator, along with the BFS-CGFS ratio, significantly affected the properties of the ceramsite. The cylinder compressive strength, bulk density, and apparent density of the ceramsite increased with the increase in the alkali activator dosage and modulus, but decreased as the BFS-CGFS ratio decreased. The water absorption rate behaved oppositely. With increasing alkali activator modulus and dosage, the ceramsite's adsorption capacity for iodine, CODCr, and methylene blue first increased and then decreased, peaking at a modulus of 1.4 and a dosage of 12%, respectively. A reduced BFS-CGFS ratio increased the adsorption capacity of the ceramsite for iodine and CODCr. Considering both mechanical and adsorption properties, the optimal composition for the ceramsite was found to be an alkali activator modulus of 1.4, a dosage of 12%, and a CGFS content of 70%. With this ratio, the use of ceramsite would not result in secondary environmental pollution. This study not only provided a new approach to enhance the utilization of CGFS but also avoided the use of calcination processes in producing ceramsite, demonstrating its potential in reducing environmental impact.

Oilfields produced water and constructed wetlands technology: a comprehensive review

ABSTRACT Produced water has elevated levels of salts, metals, and organic constituents and is seen as a waste byproduct in the oil and gas industry. This comprehensive review summarizes (1) the potential impact, (2) produced water management, and (3) identifies current research thrust areas in future efforts. Complementary treatment systems involving chemical and biological techniques offer significant advantages. The review emphasizes the application of these technologies and their performance in meeting regulatory standards. Cost, energy consumption, chemical use, and operational complexity are recognized challenges in both the water treatment industry and the oil and gas industry. It highlights the need for further research and for the optimization of processes to enhance their efficiency. The integration of conventional methods with advanced treatment processes is also explored, with a vision toward developing hybrid systems for improved treatment efficiency. Overall, complementary systems show great promise for the treatment of produced water, but further advancements, sustainability considerations, and integration with other technologies are essential for their successful implementation in large-scale applications. Maintaining expertise and awareness of water treatment issues in the oil and gas industry can help reclamation identify new technologies and solutions to technical challenges that may benefit the oilfield water treatment industry.

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

Analysis of applying machine learning methods in rock classification problems on core samples

Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely important in the early stages of field development, as it allows us to formulate a primary version of the development project, which is then refined during the course of field drilling. However, core analysis and description are an extremely labor-intensive and human-influenced works that require automation. Thus, core image research is a popular task in the oil and gas industry that requires high accuracy and care during work; especially considering the volume of images that have to be analyzed. Aim. To review and analyze existing algorithms for classifying rocks from core images based on machine learning methods; as well as to use the information obtained to formulate recommendations for the development of these algorithms. Methods. Machine learning methods, including neural networks. Results. The work analyzed existing approaches to the study of core samples. The main advantages and disadvantages of each of them were noted and, based on the findings, a plan and requirements for conducting further research on core samples using machine learning were developed. As a result, using a convolutional neural network on the U-Net architecture, the authors have trained a model to solve the problem of segmentation of core samples in daytime images and presented the results of the model operation.

How oil and gas industry are transforming with AI and ML

The Oil and Gas Industry is witnessing a major transformation due to the advent of Artificial Intelligence (AI) and Machine Learning (ML). These advances are changing operations by making things work better, cheaper and safer. Companies are using Artificial intelligence and Machine Learning to analyze large data points generated due to exploration, production and distribution activities which generate actionable insights leading to data driven decision making. AI and ML techniques for exploration, well placement optimization such as mount point selection or drilling parameters suggest based on geological data analysis including a historical performance. Using data in a real-time and predictive manner, manufacturing plants can catch early signs of equipment issues such as rising temperature encouraging proactive measures to be taken which reduces downtime and maintenance costs. Furthermore, reservoir management, hydrocarbon extraction and energy efficiency are optimized via AI-based optimization strategies.

Prediction of Sand Production from Poorly Consolidated Formations Using Artificial Neural Network: A Case Study, Nahr Umr Formation in Subba Oil Field

Sand production in poorly consolidated formations is a significant challenge to the oil and gas industry, resulting in reduced production and compromised equipment integrity. The oil and gas industry has recently become interested in data analytics because it has significantly simplified, accelerated, and reduced the cost of data visualization. The intriguing new approaches in artificial intelligence development promise to offer long-term solutions to the increasing problems affecting the petroleum industry's operations. Any completion operation in sandstone formations is based on determining whether or not a well will produce sand. It is economically unfavorable to the Nahr Umr Formation to install sand control equipment for wells that don't produce sand. This study aims to create an efficient and precise artificial neural network that can predict sanding in sandstone formations. To achieve this, we developed a two-layered ANN with a back-propagation algorithm using JMP software. The model formulation included various geological and mechanical factors that could impact sand detachment, such as Young’s modulus (YME), Poisson’s ratio, minimum and maximum horizontal stresses (SHMIN and SHMAX), pore pressure, shale volume, and formation strength. The study acquired data from both local fields, specifically the Amarah oilfield, and non-local fields from the literature. Two sets were created from the data: a training set comprising 75% of the data, and a test set comprising 25% of the data, both of which were classified. The speed and accuracy of a learning process in an Artificial Neural Network depends on the size of the data set, the number of hidden layers, and the input parameters. The statistical measures such as accuracy, confusion matrix, root mean square error, and generalized RSquare demonstrate the strong efficacy of the created Artificial Neural Network model. The results indicate a significant likelihood of sand production occurring over the perforation intervals across Su-4 and Su-5 wells. The method's unique feature is its ability to isolate shale areas and identify weak areas capable of producing sand in sandstone formations. In conclusion, it is determined that an Artificial Neural Network employing the back-propagation algorithm is a simple, reliable, and easy-to-use method for accurately predicting sand production.

CFD Simulation of the Influence of the Type of Gas Distribution in the Burners on Thermal Aerodynamic Processes in the DKVR 10-13 Boiler

Topics related to fuel combustion and its impact on the environment will never lose their relevance, as the issues of efficient combustion and emission reduction are key in power generation and environmental protection. The countries of the European Union are massively abandoning the use of natural gas as a fuel for thermal power stations. However, in Asian countries, the ease of using natural gas as the main fuel and its environmental friendliness compared to coal made it possible to widely use natural gas in industry and energy sector. Comparing natural gas with alternative combustible gases (generator, blast furnace, mine, biogas), the main conclusion that it has the most attractive characteristics for its use in industry, including energy facilities, can be drawn. Therefore, it is impossible to replace it with alternative fuels in the chemical, heavy industry and energy sector in the near future. The paper is devoted to CFD modeling of stabilized combustion without premixing in a burner with low swirl for two operating modes of the boiler unit - nominal one and at 60% capacity. The study was carried out using numerical methods with the ANSYS-Fluent application program package. The object of the study is a burner built according to the technology based on the use of jet-niche systems with gas distribution of fuel by circular jets fed perpendicularly into the flow of the oxidizer through a single-row system of holes. Hydrodynamics and heat exchange processes were chosen as the subject of research, based on the analysis of which a model of NOx generation in jet-niche systems was obtained. The authors of the paper believe that replacing the regular burners of the DKVR-10-13 water heating boiler with a jet-niche ones can contribute to better mixing of fuel and air and ensure more complete combustion. In this paper, two types of burners are considered. In one of the burners, fuel is supplied through rectangular slits, in the other – through round holes arranged in a row. Air is supplied to both burners through rectangular slits. It was determined that gas distribution through round holes increases the spraying of the mixture and increases the area of combustion products spraying. Visualization of the distribution of pressure, temperature, kinetic energy profiles of turbulent pulsations and vorticity was carried out. The obtained results indicate that there are no changes in the flow regime, flame displacement or its instability. It was determined that both the axial velocity and the tangential velocity of the flow affect the distribution of combustion products and harmful impurities such as NOx. Gas distribution in circular jets stabilizes combustion and reduces flame expansion.

Circular economy practices in the oil and gas industry: A business perspective on sustainable resource management

Circular economy practices in the oil and gas industry: A business perspective on sustainable resource management

TRANSLATION PECULIARITIES OF SLANG IN THE OIL AND GAS FIELD(BASED ON THE TERMINOLOGY OF TCO, KPO B.V. COMPANIES)

The article deals with the translation peculiarities of oil and gas slangs. It highlights the translation methods and techniques applicable for technical jargons. The descriptive method is focused as an effective way of translating the slangs used in the oil and gas field. It also reflects the challenges that technical translators face and gives the ways of solving them. The technical translation requires different approaches and methods including subject-matter and language proficiency. That’s why specialized knowledge of the field is necessary for technical translators. It’s hard to say that any philologist of the foreign language is able to translate special terms connected with the oil and gas industry as long as she/he can work on site and see the equipment or the process that is going to be translated. It is vital to remember that the language of the technical text is concise and concrete too. There shouldn’t be any ambiguity and confusion.

ЖACAНДЫ ИНТEЛЛEКТ ПEН AҚПAРAТТЫҚ ТEХНOЛOГИЯЛAРДЫҢ ИНТEГРAЦИЯCЫ: ҚAЗІРГІ ЖAҒДAЙЫ ЖӘНE ДAМУ ПEРCПEКТИВAЛAРЫ

The integration of artificial intelligence (AI) and information technology (IT) plays a key role in today's digital world, leading to revolutionary changes in various industries such as healthcare, finance, manufacturing and transportation. In this article, we will review the current state of AI and IT integration, discuss the main advantages of such integration, and identify prospects for its further development. Artificial intelligence allows you to analyze a large amount of data, which is important for analyzing the academic performance of each individual and forming an individual learning plan taking into account specific gaps in each individual area or topic. To date, it is difficult to imagine an enterprise that does not use or has not attempted to use AI in its activities. Examples of enterprises actively integrating intelligent solutions in their activities are the oil and gas industry, the energy sector, aircraft manufacturing and many others. In each of these areas, AI can significantly reduce the number of emergencies, increasing the operational reliability and safety of technological equipment and machinery.

A governance and risk management framework for project management in the oil and gas industry

The oil and gas industry operates in an environment marked by high risks, complex regulations, and significant financial investments. Effective project management in this sector requires a robust governance and risk management framework to address operational, regulatory, financial, and environmental challenges. This review proposes a comprehensive governance and risk management framework tailored specifically to the unique needs of oil and gas projects. The framework integrates governance structures that define clear roles, responsibilities, and decision-making processes, ensuring that projects align with corporate objectives and compliance standards. Central to this framework is a risk management model that identifies, assesses, and mitigates potential project risks. The model emphasizes continuous risk monitoring, utilizing advanced technologies such as predictive analytics, AI, and digital twins to forecast risks and optimize decision-making. Additionally, it advocates for proactive stakeholder engagement strategies to ensure alignment among diverse stakeholders, including government agencies, investors, and local communities. The governance aspect of the framework promotes transparency, accountability, and sustainability, ensuring that projects meet both operational goals and regulatory requirements. It also introduces agile project management techniques to enhance flexibility and responsiveness throughout the project lifecycle. By integrating technological tools like blockchain for contract management and AI for predictive maintenance, the framework addresses both governance and risk management challenges. Ultimately, the review serves as a guide for oil and gas companies to enhance project efficiency, minimize risks, and meet the demands of an increasingly complex industry landscape.