Chemistry and Technology of Fuels and Oils最新文献

This study investigates the relationship between environmental regulation and total factor carbon productivity in China’s industrial sectors. Using panel data analysis from 2000 to 2016, we find that environmental regulations significantly enhance carbon productivity. We also examine the mediating effect of environmental regulation and analyze the dynamic effects using a threshold effect model. The results reveal a non-linear relationship, where stricter regulations may increase carbon emissions beyond a certain threshold. The study emphasizes the importance of energy allocation and technology development in shaping carbon productivity outcomes. Promoting innovation, developing a clean energy system, and implementing effective environmental regulations are crucial for improving total factor carbon productivity and achieving sustainable economic growth.

With rapid economic expansion, China is faced with environmental challenges like air pollution and greenhouse gas emissions. Shifting from conventional fossil fuels to renewable energy (REN) sources is critical to facilitate sustainable development in China. Compared to coal and oil, REN such as solar and wind energy emit less carbon emissions. Fostering innovation of REN technologies is thus essential for China's green transition. This study aims to analyze the impact of REN technology innovation on China's economic growth using panel data models. The results demonstrate that advancing REN technologies significantly promotes GDP increase in China. Targeted policy incentives must be implemented to accelerate REN technology progression and adoption across the country. Transitioning towards REN systems will be instrumental for China to achieve environmental sustainability while maintaining economic growth.

The work is devoted to the study and optimal design of the separation of gas mixtures (CO₂/CH₄ and CO₂/N₂) by the adsorption method. Synthetic zeolites NaX were used as adsorbents. Binary model mixtures of the gases were used: CO₂50%, CH₄50%, and N₂50%, CH₄50% by volume. The diffusion coefficients were determined. The experiments were carried out at a temperature of 295 K. A complete mathematical model of the adsorption process was developed.

The paper considers approaches, principles, and results of modeling the quality parameters of petroleum bitumen using machine learning algorithms based on recurrent neural networks. It is shown that machine learning algorithms can be effectively used in practice for oil refining processes. Various problems involved in data processing, as well as selection of variables and suitable neural network architecture for solving a particular problem, are considered. Further research directions are outlined.

The key to the development of oil and gas resources in the Tamulangou Formation in the Huhehu Sag of the Hailar Basin lies in the understanding of sedimentary characteristics and the division of volcanic-sedimentary cycles. These cycles are divided into five sedimentary systems, namely alluvial fans, braided rivers, fan deltas, braided river deltas, and lacustrine systems. Based on the lithological characteristics, sedimentary structures, geometric forms, and definitions of the Huhehu Sag. The volcanic-sedimentary sequences of the Tamulangou Formation are dominated by intermediate-basic volcanic rocks and acidic volcanic rocks and they are located at the uppermost part of the Huhehu Sag. The Huhehu Sag gou Formation contains thin-bedded sedimentary rocks and locally thick-bedded sedimentary rocks, with localized development of the 148 Ma Manitu Formation volcanic-sedimentary sequence. The source rocks in the Huhehu Sag are of moderate to good quality. And the main controls on hydrocarbon accumulation are the distribution of hydrocarbon source rocks, development of fault zones, characteristics of fan deltas, and volcanic-sedimentary processes.

Over the past five years a significant number of technologies to conversion processes to yield new products high-value added petrochemical products have appeared: plastic waste can be returned for processing into value-added petrochemical products, including aromatic hydrocarbons, hydrogen, syngas, and bio feedstock using a variety of technologies including thermochemical, catalytic conversion, and chemolysis. The article discusses the market prospects for the processing of polymer waste and the production of secondary polymers and the regulation and incentive issues and presents the experience of LINK (the production and service center of the LUKOIL Company) on the treatment of polymer waste and assessment of the carbon footprint.

The article considers stochastic mathematical models for thermolysis kinetics of complex oil-like hydrocarbon systems using mathematical statistics and the theory of random processes. Mathematical modeling shows that the thermolysis process is collective, nonergodic, and nonstationary. The criteria for which hydrocarbon systems obey the first order and Avrami kinetic laws are established. Using the example of thermolysis of high-viscosity Al’shacha oil, it is shown that the kinetics of the release of gaseous products of thermolysis are better described by models of stationary kinetics, and the yield of residues and distillates is better described by models of nonstationary kinetics. The established regularities can be used in modeling thermal and thermocanalytic processes of petrochemistry and oil refining.

In order to improve the accuracy of cementing quality evaluation for ultra-low density cement, a full-scale cementing quality evaluation model well group was constructed based on the real underground environment and the requirements for cementing quality and cement filling. A calibration method for cementing quality evaluation indicators was established, and the influence of factors such as logging time and cement density on cementing quality evaluation indicators was analyzed. The results were compared with theoretical calculations, The experimental results are in good agreement with the theoretical calculation results. According to the experimental results, the evaluation indicators of ultra-low density cement cementing quality are inversely correlated with cement density & logging time. The research results indicate that the accuracy and pertinence of cementing quality evaluation can be significantly improved by applying the ultra-low density cement slurry cementing quality evaluation model well group and verifying the ultra-low density cement cementing quality evaluation indicators.

Thin oil layers have become the focus of oil and gas resources exploration due to the large number of oil layers and rich reserves. Fuyu Formation in the sag area of an oil field in the east belongs to a low porosity and low permeability reservoir, and the production of vertical wells is relatively low. The application of horizontal well technology can effectively improve the production. The third member of Fuyuquan Formation has the characteristics of thin interbed reservoir, which increases the difficulty of horizontal well implementation. The resolution of seismic data is limited, and the difference of geophysical response between reservoir and non-reservoir is small, which makes it difficult to solve the problem of thin interbed prediction by wave impedance inversion and pre-stack simultaneous inversion. The application of prestack geostatistical inversion technology can improve the vertical resolution of reservoir prediction, and then accurately depict the spatial distribution of the reservoir. In this paper, taking well F area as the test area, combining with regional geological knowledge, the seismic inversion results with high vertical resolution are obtained through reservoir sensitive parameter analysis and prestack geostatistics inversion technology. It also guides the design of horizontal well trajectory and monitors the change of horizontal well trajectory in real time during implementation. The results show that using pre-stack geostatistics inversion technology can greatly improve the drilling rate of thin oil layers in horizontal wells.

Accurate identification of gas-liquid two-phase flow patterns during oil and gas drilling is critical to analyzing bottom hole pressure, detecting overflows in time, and preventing blowout accidents. Since the gas-liquid two-phase flow has deformable interfaces, resulting in complex gas-liquid two-phase flow patterns, the existing gas-liquid two-phase flow patterns are limited in width in terms of pipe diameter and incline, leading to adaptation problems in experimental flow patterns and mechanistic models. Machine learning methods provide potential tools for solving gas-liquid two-phase flow pattern identification. In this paper, a sample database with 5879 data points was established by reviewing and organizing existing literature focusing on normal pressure and temperature, and air-water experimental conditions to provide a data-preparation for the relationship between gas and liquid velocities, pipe diameter and incline characteristics and flow pattern objectives. Four machine learning models, including K-Nearest Neighbor, Naïve Bayes, Decision Tree and Random Forest, were investigated, and each model was trained and tested using a sample database to reveal the performance of four types of supervised machine learning methods, representing similarity, probability, inductive inference and ensemble-learning principles, for gas-liquid two-phase flow pattern recognition, and the prediction accuracy was 0.86, Naïve Bayes is 0.56, Decision Tree is 0.89 and Random Forest 0.97. Comprehensive analysis of each model confusion matrix shows that the machine learning method has the best recognition of dispersed bubble flow, better recognition of slug flow, and the worst recognition of churn flow among the nine flow patterns which proves the controversial nature of the mechanism model in the transition from slug flow to churn flow. This paper uses experimental data as model input features, making the machine learning-based gas-liquid two-phase flow pattern identification model meaningful for practical engineering applications, and also demonstrating the feasibility of using supervised machine learning methods for gas-liquid two-phase flow pattern identification at normal pressure and temperature, wide-range of pipe diameter and incline.