Search Results (86)

The white oil-bearing rose (R. alba L.) is the second of the industrially important rose species for Bulgarian rose cultivation and essential oil production. In recent years, the interest in white oil-bearing rose has increased, following the worldwide trend for searching for new aromatic alternatives. Therefore, the purpose of the current research is to evaluate the volatile compounds profile of fresh R. alba L. flowers using headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC/MS). More than 75 individual compounds were identified and quantified using HS-SPME-GC/MS. The study revealed that the aroma-bearing fraction of rose volatiles consists mainly of monoterpene alcohols; 2-phenylethanol was the most abundant component (8.4–33.9%), followed by geraniol (12.8–32.5%) and citronellol + nerol (17.7–26.5%). Linalool, α-pinene, β-myrcene, and rose oxides were also observed in low concentrations. The stearopten fraction in the HS phase was observed in low concentration, with main representatives nonadecane + nonadecene, heptadecane, heneicosane, and tricosane. The HS-GC profile of the R. alba fresh flowers shows distinct differences in relative abundance of the components between the two studied clones of the population, as well as between volatiles in petals and in the whole blossom. The absence of some undesirable components, such as allergenic and potentially carcinogenic methyl eugenol in fresh R. alba blossom, makes white oil-bearing rose a promising alternative to R. damascena in perfumery, natural cosmetics, and aromatherapy. Full article

Enhancing the resilience of the oil and gas industry chain is essential for achieving sustainable energy development amid global industrial restructuring and the accelerating low-carbon transformation. This study identifies the core contradictions in the development of China’s OGI and constructs a comprehensive evaluation index system to assess the resilience of the industry from the four sustainability-aligned dimensions of resistance, recovery, innovation, and transformation. Using the entropy weight comprehensive evaluation model, obstacle degree model, and coupling coordination degree model, the resilience performance of China’s OGI chain is evaluated from 2001 to 2022. The results show a significant upward trend in overall resilience, with evident stage characteristics. Resistance remains relatively stable, recovery shows the most improvement, innovation steadily increases, and transformation accelerates after 2019, particularly in response to China’s dual carbon goals. Key barriers include limited CCUS deployment and insufficient downstream innovation capacity. The improved coupling coordination among resilience subsystems highlights enhanced systemic synergy. These findings offer valuable implications for strengthening the sustainability and security of energy supply chains under climate and geopolitical pressures. Full article

Understanding the rheological properties of fresh cement slurries is essential to maintain optimal pumpability, achieve dependable zonal isolation, and preserve long-term well integrity in oil and gas cementing operations and the 3D printing cement and concrete industry. However, accurately and efficiently modeling the rheological behavior of cement slurries remains challenging due to the complex fluid properties of fresh cement slurries, which exhibit non-Newtonian and thixotropic behavior. Traditional numerical solvers typically require mesh generation and intensive computation, making them less practical for data-scarce, high-dimensional problems. In this study, a physics-informed neural network (PINN)-based framework is developed to solve the governing equations of steady-state cement slurry flow in a tilted channel. The slurry is modeled as a non-Newtonian fluid with viscosity dependent on both the shear rate and particle volume fraction. The PINN-based approach incorporates physical laws into the loss function, offering mesh-free solutions with strong generalization ability. The results show that PINNs accurately capture the trend of velocity and volume fraction profiles under varying material and flow parameters. Compared to conventional solvers, the PINN solution offers a more efficient and flexible alternative for modeling complex rheological behavior in data-limited scenarios. These findings demonstrate the potential of PINNs as a robust tool for cement slurry rheological modeling, particularly in scenarios where traditional solvers are impractical. Future work will focus on enhancing model precision through hybrid learning strategies that incorporate labeled data, potentially enabling real-time predictive modeling for field applications. Full article

In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. The localized air pollutant levels and greenhouse gas emissions of the three enterprises were calculated. The synergistic effects between the end-of-pipe emission reductions for air pollutants and greenhouse gas emissions were analyzed using the pollutant reduction synergistic and cross-elasticity coefficients, including technology comparisons (e.g., acrylonitrile gas incineration (AOGI) technology vs. traditional flare). Based on these data, we used the SimaPro software and the CML-IA model to conduct a life cycle environmental impact assessment regarding the production and upstream processes of their unit products. By combining the life cycle method and the scenario simulation method, we predicted the trends in the environmental impacts of the three chemical enterprises after the implementation of low-carbon development policies in the chemical industry in 2030. We also quantified the synergistic effects of localized air pollutant and greenhouse gas (GHG) emission reductions within the low-carbon development scenario by using cross-elasticity coefficients based on life cycle environmental impacts. The research results show that, for every ton of air pollutant reduced through end-of-pipe treatment measures, the HS Chlor-Alkali enterprise would increase its maximum CO₂ emissions, amounting to about 80 tons. For SK Ethylene, the synergistic coefficient for VOC reduction and CO₂ emissions when using AOGI thermal incineration technology is superior to that for traditional flare thermal incineration. The activities of the three enterprises had an impact on several environmental indicators, particularly the fossil fuel resource depletion potential, accounting for 69.48%, 53.94%, and 34.23% of their total environmental impact loads, respectively. The scenario simulations indicate that, in a low-carbon development scenario, the overall environmental impact loads of SH Petrochemical (refining sector), SK Ethylene, and HS Chlor-Alkali would decrease by 3~5%. This result suggests that optimizing the upstream power structure, using “green hydrogen” instead of “grey hydrogen” in hydrogenation units within refining enterprises, and reducing the consumption of electricity and steam in the production processes of ethylene and chlor-alkali are effective measures in reducing carbon emissions in the chemical industry. The quantification of the synergies based on life cycle environmental impacts revealed that there are relatively strong synergies for air pollutant and GHG emission reductions in the oil-refining industry, while the chlor-alkali industry has the weakest synergies. Full article

Air quality and environmental issues have gained attention from countries and organizations worldwide over the past several decades. In recent years, carbon peak and carbon neutrality have been mentioned at many international conferences and meetings aimed at reducing and controlling environmental challenges. This study focuses on trend analysis and expectations for the duration of control for environmental air quality (EAQ) indicators, assesses the current EAQ conditions across global countries, and presents reasonable suggestions for environmental control. The study begins by examining the annual, per capita, and per square meter (${\mathrm{m}}^2$) carbon dioxide (CO₂) emission peak and standardizations, where carbon standardization is a replacement for carbon neutrality. A similar quantitative methodology was employed to assess classical air quality factors such as sulfur dioxide (SO₂) and nitrogen oxides (NO_x). The findings suggest that the average control year length (ACYL) of NO_x is longer than that of SO₂, and the ACYL of SO₂ is, in turn, longer than that of CO₂. From an energy structure perspective, regressions results indicate that biofuel and wind power contribute to improvements in EAQ, while coal, oil, and gas power exert negative impacts. Moreover, a long-term EAQ model utilizing an adjusted max–min normalization method is proposed to integrate various EAQ indicators. This study also presents an EAQ ranking for global countries and recommends countries with critical EAQ challenges. The results demonstrate that it is plausible to control EAQ factors at an excellent level with advances in control technologies and effective measures by government, industries, and individuals. Full article

Toxic elements are considered a significant threat to public health in oil-producing countries. Western Kazakhstan is experiencing serious environmental problems due to the development of the oil and gas industry. This study aimed to assess the concentrations of toxic trace elements—aluminum (Al), arsenic (As), beryllium (Be), cadmium (Cd), mercury (Hg), and lead (Pb)—in the hair of children residing in Kazakhstan’s oil and gas-producing region, and to evaluate the relationship between the concentration of toxic elements and the remoteness of their residence from oil and gas fields. A cross-sectional analysis was conducted involving 1595 school-aged children. Element levels in hair samples were quantified using inductively coupled plasma mass spectrometry (ICP-MS). The association between trace element concentrations and residential distance from oil and gas fields was examined across three distance-based groups and further analyzed through multiple linear regression. The highest concentration of Al = 4.824 μg/g and Hg = 0.096 μg/g was found in the hair of children living close to oil and gas fields (0–16 km). A decrease in levels of Al (−0.072 (CI: −0.109; −0.036)) and Hg (−0.293 (CI: −0.343; −0.243)) is associated with increasing distance from oil and gas fields. As, Cd, and Pb had the lowest median concentrations in the hair of children living near oil and gas fields (0.030, 0.010, and 0.122 µg/g, respectively). There is a tendency for levels of As, Cd, and Pb to increase with distance from the fields (0.064 (CI: 0.039; 0.089), 0.093 (CI: 0.045; 0.141), and 0.244 (CI: 0.202; 0.287), respectively). Our findings indicate the need for biomonitoring of toxic elements to determine long-term temporal trends in the influence of toxic trace elements on the health of the child population of Western Kazakhstan. Full article

The global demand for oils and lipids, particularly those derived from vegetable sources with high polyunsaturated fatty acid content, has posed significant challenges for the food industry. This trend is largely driven by growing consumer awareness of health and nutrition. To meet this demand, it is essential to not only identify richer sources of lipids but also develop efficient, sustainable, and environmentally friendly methods for their extraction, isolation, and characterization. In this context, the present work provides a comprehensive review of current perspectives on the extraction, isolation, and identification of lipids and fatty acids, comparing conventional and green methodologies for food applications. Ideally, analytical and processing methodologies for obtaining food-grade materials should prioritize low energy consumption, minimal or no use of hazardous substances, and the generation of non-polluting residues, thereby safeguarding both human health and the environment. In recent years, green extraction techniques have emerged as promising alternatives to conventional methods, offering partial or complete replacements, such as ultrasound-assisted extraction, microwave-assisted extraction, supercritical and subcritical fluid extraction, and others. However, significant advancements are still required to fully address these concerns. Techniques such as chromatography and spectrometry play pivotal roles in the isolation and identification process, especially gas chromatography coupled with mass spectrometry or with flame ionization detectors; while separating individual fatty acids based on their chain length and degree of unsaturation, reversed-phase high-performance liquid chromatography (HPLC) is quite a helpful approach. Furthermore, the isolation and structural elucidation of fatty acids are critical steps in ensuring the nutritional quality and commercial viability of lipid products. Full article

Biodiesel derived from waste cooking oil (WCO) and animal fats is a promising alternative to fossil fuels, offering environmental benefits and renewable energy potential. However, a detailed understanding of its combustion characteristics at the droplet scale is essential for optimizing its practical application. This study investigates the combustion behavior of biodiesel–diesel blends (B5, B10, B15, B20, B25, B50, B75) and neat fuels (B0 and B100) by analyzing combustion rates, pre-ignition time, burning time, droplet morphology, and puffing characteristics. The results demonstrate that biodiesel concentration strongly influences combustion dynamics. Higher blends (B50, B75) exhibit enhanced steady combustion rates due to increased oxygen availability, while lower blends (B5–B25) experience stronger puffing events, leading to greater secondary droplet formation. The global combustion rate follows a non-linear trend, peaking at B10, decreasing at B25, and rising again at B50 and B75. Pre-ignition time increases with biodiesel content, while burning time exhibits an inverse relationship with combustion rate. Four distinct puffing mechanisms were identified, with lower blends producing finer secondary droplets and higher blends forming larger droplets. Puffing characteristics were evaluated based on puffing occurrences, intensity, and effectiveness, revealing that puffing peaks at B25 in occurrence and at B10 in intensity, while higher blends (B50, B75) exhibit notable puffing effectiveness. This study addresses a critical research gap in droplet-scale combustion of WCO and animal fat-derived biodiesel across a wide range of blend ratios (B5–B75). The findings provide key insights for optimizing biodiesel formulations to improve fuel spray atomization, ignition stability, and combustion efficiency in spray-based combustion systems, such as diesel engines, gas turbines, and industrial burners, bridging fundamental research with real-world applications. Full article

Reservoir geological modeling plays a crucial role in characterizing the spatial distribution and heterogeneity of subsurface reservoirs. The exploration of deep oil and gas resources is not only a global trend in the oil industry but also an inevitable choice for China to ensure energy security and achieve sustainable development in the oil and gas industry. Oil and gas exploration and development technologies have also made continuous breakthroughs, providing strong support for the sustained increase in China’s deep and ultra-deep oil and gas production. Deep and ultra-deep oil and gas reservoirs exhibit high levels of heterogeneity, which are governed by the original sedimentation processes and have a significant impact on oil and gas migration and accumulation. However, traditional pixel-based stochastic reservoir modeling encounters challenges when attempting to effectively simulate multiple facies simultaneously or objects with intricate internal hierarchical architectures. To address the characterization of highly heterogeneous deep and ultra-deep oil and gas reservoirs, this study defines unit architecture bodies, such as point bars, braided rivers, and mouth bars, incorporating internal nested hierarchies. Furthermore, a novel object-based stochastic modeling method is proposed, which leverages seismic and well logging interpretation data to construct and simulate reservoir bodies. The methodology is rooted in the unit element theory. In this approach, sedimentary facies models are stochastically constructed by selecting appropriate unit elements from a database of different sedimentary environments using Sequential Indicator Simulation. The modeling process is constrained by time sequence, event, and sedimentary microfacies distributions. Additionally, the porosity and permeability of each microfacies in the reservoir model are quantitatively characterized based on statistics derived from porosity and permeability data of different strata, sedimentary microfacies, and rock facies in the study area. To demonstrate the superiority and reliability of this novel modeling method, a modeling case is presented. The case utilizes braided river unit elements as objects for the stochastic simulation of the target reservoir. The results of the case study highlight the advantages and robustness of the proposed modeling approach. Full article

Based on productivity and efficiency indicators, we investigated the performance of the Brazilian oil and gas exploration industry, comparing the performance of this sector with other industrial sectors. We associate productivity with the concept of total factor productivity (TFP), while efficiency is measured using the stochastic frontier production model. Our sample was assembled from the Annual Industrial Survey (PIA) for 29 Brazilian industrial sectors from 2007 to 2019, period of data availability. The results derived from both methods allow us to affirm that the policies resulting from the Pre-Salt have significantly boosted the oil and natural gas extraction sector in terms of technological progress and efficiency. Between 2007 and 2009, the sector was among the least efficient, ranking 29th. However, in 2019 it reached first place in terms of efficiency. This structural change, which began in 2010 as a result of the technological innovations resulting from investments in R&D, has undergone a change since 2010, reflected in the upward trend towards pre-salt exploration promoted by Petrobras, in Rio de Janeiro, Brazil, as well as the new regulatory framework and government incentives for oil exploration in Brazil. Un-fortunately, these productivity gains have not been exported to other branches of industry connected to the oil industry. Full article

The modern construction of transmission networks for transporting energy resources (e.g., crude oil, gas, hydrogen) or electricity is increasingly being carried out using trenchless technologies. Trenchless methods significantly reduce the need for extensive earthworks; however, they consequently generate substantial amounts of drilling waste. This waste consists primarily of a mixture of spent drilling fluids and drill cuttings. Due to the volume and composition of the waste, along with the rapidly increasing costs of waste disposal, the trenchless technology industry faces significant economic and environmental challenges related to circular economy principles in waste management. This article presents an analysis of trenchless construction methods for underground transmission networks, with particular emphasis on the quantity and quality of the generated drilling waste. Furthermore, research is conducted to develop a cationic flocculant based on polyvinylamine, designed to eliminate the harmful coagulants in drilling waste treatment technology. Based on the conducted studies, we propose a closed-loop waste management system for trenchless technologies. The implementation of circular economy principles, along with the integration of drilling fluid treatment systems with photovoltaic panels and energy storage units, enhances the energy efficiency of drilling waste treatment processes and aligns with global trends in the adoption of renewable energy sources (RESs). Full article

Ultrasonic flow meters (UFMs) by transit time are ubiquitous in industrial applications, mainly for their versatility and practicality. They are widely used in gas and liquid installations, such as the oil and gas industry or feedwater systems in nuclear power plants. Computational fluid dynamics (CFD) techniques can be used as a tool to potentially improve the ultrasonic flow measurements. CFD may contribute to predicting the velocity profile and the profile factor in disturbed flows, integrating fluid flow and acoustic ray, improving the calibration of UFMs, or assisting in design optimization. This communication presents the working principle of the UFM, discusses how CFD can be used as a tool to support improvements, and shows relevant trending fields that deserve further investigation to promote significance on this subject. Full article

Multi-stage hydraulic fracturing is a key technology adopted in the energy industry to make shale gas and shale oil fields profitable. Post-frac fluid soaking before putting wells into production has been found essential for enhancing well productivity. Finding the optimum time to terminate the fluid-soaking process is an open problem to solve. Post-frac shut-in pressure data from six wells in two shale gas fields were investigated in this study based on pressure transient analysis (PTA) to reveal fluid-soaking performance. It was found that pressure-derivative data become scattering after 1 day of well shut in. The overall trend of pressure-derivative data after the first day of well shut in should reflect the effectiveness of fluid soaking. Two wells exhibited flat (zero-slope) pressure derivatives within one week of fluid soaking, indicating adequate time of fluid soaking. Four wells exhibited increasing pressure derivatives within one week of fluid soaking, indicating inadequate time of fluid soaking. This observation is consistent with the reported well’s Estimated Ultimate Recovery (EUR). This study presents a new approach to the assessment of post-frac fluid-soaking performance with real-time shut-in pressure data. Full article

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development. Full article

The genus of Nitraria L. are Tertiary-relict desert sand-fixing plants, which are an important forage and agricultural product, as well as an important source of medicinal and woody vegetable oil. In order to provide a theoretical basis for better protection and utilization of species in the Nitraria L., this study collected global distribution information within the Nitraria L., along with data on 29 environmental and climatic factors. The Maximum Entropy (MaxEnt) model was used to simulate the globally suitable distribution areas for Nitraria L. The results showed that the mean AUC value was 0.897, the TSS average value was 0.913, and the model prediction results were excellent. UV-B seasonality (UVB-2), UV-B of the lowest month (UVB-4), precipitation of the warmest quarter (bio18), the DEM (Digital Elevation Model), and annual precipitation (bio12) were the key variables affecting the distribution area of Nitraria L, with contributions of 54.4%, 11.1%, 8.3%, 7.4%, and 4.1%, respectively. The Nitraria L. plants are currently found mainly in Central Asia, North Africa, the neighboring Middle East, and parts of southern Australia and Siberia. In future scenarios, except for a small expansion of the 2030s scenario model Nitraria L., the potential suitable distribution areas showed a decreasing trend. The contraction area is mainly concentrated in South Asia, such as Afghanistan and Pakistan, North Africa, Libya, as well as in areas of low suitability in northern Australia, where there was also significant shrinkage. The areas of expansion are mainly concentrated in the Qinghai–Tibet Plateau to the Iranian plateau, and the Sahara Desert is also partly expanded. With rising Greenhouse gas concentrations, habitat fragmentation is becoming more severe. Center-of-mass migration results also suggest that the potential suitable area of Nitraria L. will shift northwestward in the future. This study can provide a theoretical basis for determining the scope of Nitraria L. habitat protection, population restoration, resource management and industrial development in local areas. Full article