Search Results (1,766)

Tight oil is an important resource replacement in the petroleum industry, with the reserves of Type-II energy accounting for over 40%. However, these reservoirs have small pore throats and complex structures, and their wettability directly affects the EOR by affecting the occurrence of crude oil and multiphase flow mechanisms. In response to an unclear understanding of the evolution mechanism of wettability after energy replenishment in tight reservoirs with different reservoir formation conditions, the evolution law of wettability in different energy replenishment media for tight type-II reservoirs is evaluated by performing wettability experiments and nuclear magnetic resonance experiments, and the mechanism of differential changes in wettability after energy replenishment in different media is elucidated. The results show that the block with well-developed pores and good connectivity (Block: Z401) had the smallest in situ wetting angle, ranging from 27.1° to 30.4°, and that the interface effect had a small impact, resulting in a small change in the wetting angle after energy replenishment. The wetting angle of the developmental intersection block (Block: G93) is the highest, ranging from 36.6° to 46.4°. The connected pore and throats fully interact with the medium at the interface, resulting in a significant change in the wetting angle. In addition, after natural gas energy supplementation, the principle of similar solubility causes a significant change in the wetting angle of the pore throat interface after adsorption, with a maximum angle of 19.6°. The change in the wetting angle change of the CO₂ mixed-phase principle is in the middle, at about 13.6°, while the change in the wetting angle is minimal after N₂ replenishment, around 10°. The research results improve our understanding of the basic theory of tight oil supplementary energy development and have important practical significance. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

This article discusses how various factors can affect the accuracy of U, Th, and K elemental content measurements in natural gamma spectroscopy logs. These factors include errors in the measured energy spectrum, degradation of energy resolution, and spectrum drift. Currently, there is limited research on quantifying the individual impact of each factor on measurement accuracy. To address this gap, the study proposes a methodology that combines energy spectrum data sampling and single-factor quantitative analysis. This approach allows for a more precise understanding of how each factor influences the accuracy of the measurements. The results of the study have important implications for improving the accuracy of U, Th, and K content measurements in applications such as the oil and gas industry. Full article

After heavy snowfall in the Uinta Basin, Utah, elevated surface ozone occurs if a cold-air pool persists and traps emissions from oil and gas industry operations. Sunlight and actinic flux from a high-albedo snowpack drive ozone buildup via photolysis. Snow coverage is paramount in initiating the cold pool and driving ozone generation. Its depth is critical for predicting ozone concentrations. The Basin’s location leeward of the Wasatch Mountains provides conditions for a precipitation shadow, where sinking air suppresses snowfall. We analyzed multiple years of ground-based snow depth measurements, surface ozone data, and meteorological observations; we found that ozone levels track with snow coverage, but diagnosing a shadow effect (and any impact on ozone levels) was difficult due to sparse, noisy data. The uncertainty in linking snowfall variation to ozone levels hinders forecast quality in, e.g., machine-learning training. We highlight the importance of a better understanding of regional variation when issuing outlooks to protect the local economy and health. A wider sampling of snow depth across the Basin would benefit operational forecasters and, likely, predictive skill. Full article

Porous media gas bearings utilize gas as a lubricating medium to achieve non-contact support technology. Compared with traditional liquid-lubricated bearings or rolling bearings, they are more efficient and environmentally friendly. With the uniform gas film pressure of gas bearings, the rotating shaft can achieve mechanical motion with low friction, high rotational speed, and long service life. They have significant potential in improving energy efficiency and reducing carbon emissions, enabling oil-free lubrication. By eliminating the friction losses of traditional oil-lubricated bearings, porous media gas bearings can reduce the energy consumption of industrial rotating machinery by 15–25%, directly reducing fossil energy consumption, which is of great significance for promoting carbon neutrality goals. They have excellent prospects for future applications in the civil and military aviation fields. Based on the three-dimensional flow characteristics of the bearing’s fluid domain, this paper considers the influences of the transient flow field in the variable fluid domain of the gas film and the radial pressure gradient of the gas film, establishes a theoretical model and a three-dimensional simulation model for porous media gas bearings, and studies the static–dynamic pressure coupling mechanism of porous media gas bearings. Furthermore, through the trial production of bearings and performance tests, the static characteristics are verified, and the steady-state characteristics are studied through simulation, providing a basis for the application of gas bearings made from porous media materials in the civil and military aviation fields. Full article

Nitrile rubber (NBR) exhibits excellent oil resistance, wear resistance, gas barrier properties, and mechanical properties. On the other hand, lignin, a by-product of the pulp and paper industry, can serve as an ideal substitute for carbon black as a reinforcing agent for rubber. However, when NBR is directly compounded with lignin, direct compounding fails to achieve the desired reinforcing effect due to poor dispersion of lignin in the NBR matrix and poor compatibility with the NBR phase. In this paper, carboxyl groups were introduced via cyano group hydrolysis. By controlling the hydrolysis time, we successfully prepared two types of carboxylated NBR with different carboxyl contents. Subsequently, the carboxylated NBR was processed into lignin/NBR composites via dry blending. The results indicated that the introduction of carboxyl groups endowed NBR with higher polarity and reactivity, significantly enhancing the interfacial compatibility between lignin and the rubber matrix. The mechanical properties of the composite were greatly improved, with the mechanical strength increasing from 4.5 MPa without carboxyl groups to 13.8 MPa with high carboxyl content. The good dispersion of lignin also significantly improved the thermal stability of the composite. The carboxylation modification strategy of NBR provides a new approach for preparing high-performance NBR/biomass composites. Full article

The faults in the post-rift period have an important controlling effect on the migration and accumulation of oil and gas in the shallow strata of Bohai Bay Basin. Based on the seismic interpretation data of Bozhong 19-6 Structural Belt, this paper analyzes the geometric characteristics and growth history of the faults in the post-rift period and inverts the tectonic paleostress that caused the fault activities in the post-rift period. Finally, the developmental characteristics of the faults in the post-rift period are deeply understood from three aspects: fault geometry, kinematics, and dynamics. In the study area, the trend of post-rift faults are mainly east–west, followed by NEE. According to the fault activity, it can be divided into three types: newly formed faults, long-term active faults, and deep-linked faults. The latter two types are faults that existed before and then reactivated during post-rifted period. The inversion result of the Neogene is the strike-slip stress field, showing that the intermediate principal stress axis (σ₂) is oriented vertically, the minimum principal stress (σ₃) is oriented N170°, the maximum principal stress axis (σ₁) is oriented N80°, and σ₃/σ₁ = 0.24, σ₂/σ₁ = 0.62. The data used in this inversion method is easily obtained in the oil and gas industry, and the inversion results can provide an important reference for analyzing the regional tectonic evolution and clarifying the fault activity at the key moment of oil and gas accumulation. Full article

Using high-frequency financial data, this study investigates volatility spillovers between five renewable energy subsectors (wind, solar, geothermal, bioenergy, and fuel cells), five conventional energy markets (oil, gas, coal, uranium, and gasoline), and carbon emissions for five industrial sectors (power, industry, ground transportation, domestic aviation, and residential) based on a Diebold–Yilmaz VAR-based spillover framework. The results document that the industry and power sectors are the key players in the transmission effects of carbon shocks. In contrast, the reverse is true for the residential and aviation sectors. For renewable energy, fuel cells, and geothermal power, strong forward linkages appear to significantly reduce carbon emissions, while reverse linkages that increase carbon emissions in response to shocks in clean-energy and carbon-intensive industries are relatively high for coal and oil. We also find that the total volatility connectedness exceeds 84%, indicating significant systemic risk transmission. The clean-energy subsectors, particularly wind and solar, now compete in fossil-fuel markets during geopolitical crises. Applying the DCC-GARCH t-copula method to assess portfolio hedging strategies, we find that fuel cell and geothermal assets are the most effective in hedging against volatility in fossil-fuel prices. In contrast, nuclear and gas assets provide benefits from diversification. These results underscore the growing strategic importance of clean energy in mitigating sector-specific emission risks and fostering resilient energy systems in alignment with the United States’ net-zero carbon goals. Full article

Amaranth is important for the agro-industrial complex. However, when extracting flour and oil from seeds, a lot of waste remains. Waste recycling by co-pyrolysis aims at obtaining new products with high added value. This study examined a combination of A. cruentus (AC) residues and low-density polyethylene (LDPE) waste. The addition of polymer was aimed at obtaining hydrocarbon-rich pyrolysis liquid and biochar. Pyrolysis was performed on an experimental setup, along with thermogravimetry–Fourier infrared spectroscopy–gas chromatography mass spectrometry (TG-FTIR-GC MS), to examine the thermochemical conversion. Experiments were carried out using a thermogravimetric analyzer at heating rates of 5, 10, and 20 °C/min. The average activation energy values for the pyrolysis of the AC/LDPE blend by the Ozawa–Flynn–Wall (OFW) and Kissinger–Akahira–Sunose (KAS) techniques were 301.39 kJ/mol and 287.69 kJ/mol, respectively. A visual examination of the correlations of the kinetic parameters of AC/LDPE was carried out using the Kriging method. The pyrolysis liquid from AC contains 38.14% hydrocarbons, with the main part being aliphatic hydrocarbons. During the pyrolysis of the AC/LDPE mixture, hydrocarbons were found in the resinous and waxy organic fractions of the pyrolysis liquid. The composition and properties of AC and AC/LDPE biochar are similar, and they can both be applied to agriculture. Full article

Protective coatings are essential for extending the service life of components exposed to harsh conditions, such as pipes used in industrial systems, where wear and corrosion remain constant challenges. This study explores the development of a nano-sized TiO₂-reinforced electroless nickel-based ternary (Ni-W-P) alloy and composite coating on API X60 steel, a high-strength carbon steel pipe grade widely used in oil and gas pipelines, using an alkaline hypophosphite-reduced bath. The surface morphology, microstructure, elemental composition, structure, phase evolution, adhesion, and roughness of the coatings were analyzed using optical microscopy, FESEM, EDS, XRD, AFM, cross-cut tape test, and 3D profilometry. The tribological performance was evaluated via Vickers microhardness measurements and reciprocating wear tests conducted under dry conditions at a 5 N load. The TiO₂ nanoparticle-reinforced composite coating achieved a consistent thickness of approximately 24 µm and exhibited enhanced microhardness and reduced coefficient of friction (COF), although the addition of nanoparticles increased surface roughness (S_a). Annealing the electroless composites at 400 °C led to a significant improvement in their tribological properties, primarily owing to the grain growth, phase transformation, and Ni₃P crystallization. XRD analysis revealed phase evolution from an amorphous state to crystalline Ni₃P upon annealing. Both the alloy and composite coatings exhibited excellent adhesion performances. The combined effect of TiO₂ nanoparticles, tungsten, and Ni₃P crystallization greatly improved the wear resistance, with abrasive and adhesive wear identified as the dominant mechanisms, making these coatings well suited for high-wear applications. Full article

Power transformers are critical assets in electrical power systems, yet their fault diagnosis often relies on conventional dissolved gas analysis (DGA) methods such as the Duval Pentagon and Triangle, Key Gas, and Rogers Ratio methods. Even though these methods are commonly used, they present limitations in classification accuracy, concurrent fault identification, and manual sample handling. In this study, a framework of optimized machine learning algorithms that integrates Chi-squared statistical feature selection with Random Search hyperparameter optimization algorithms was developed to enhance transformer fault classification accuracy using DGA data, thereby addressing the limitations of conventional methods and improving diagnostic precision. Utilizing the R2024b MATLAB Classification Learner App, five optimized machine learning algorithms were trained and tested using 282 transformer oil samples with varying DGA gas concentrations obtained from industrial transformers, the IEC TC10 database, and the literature. The optimized and assessed models are Linear Discriminant, Naïve Bayes, Decision Trees, Support Vector Machine, Neural Networks, k-Nearest Neighbor, and the Ensemble Algorithm. From the proposed models, the best performing algorithm, Optimized k-Nearest Neighbor, achieved an overall performance accuracy of 92.478%, followed by the Optimized Neural Network at 89.823%. To assess their performance against the conventional methods, the same dataset used for the optimized machine learning algorithms was used to evaluate the performance of the Duval Triangle and Duval Pentagon methods using VAISALA DGA software version 1.1.0; the proposed models outperformed the conventional methods, which could only achieve a classification accuracy of 35.757% and 30.818%, respectively. This study concludes that the application of the proposed optimized machine learning algorithms can enhance the classification accuracy of DGA-based faults in power transformers, supporting more reliable diagnostics and proactive maintenance strategies. Full article

Wireless sensor networks (WSNs) have gained significant attention across various industries and scientific fields. Localization, a crucial aspect of WSNs, involves accurately determining node positions to track events and execute actions. Despite the development of numerous localization algorithms, real-world environments pose challenges such as anisotropy, noise, and faults. To improve accuracy amidst these complexities, researchers are increasingly adopting advanced methodologies, including soft computing, software-defined networking, maximum likelihood estimation, and optimization techniques. Our comprehensive review from 2020 to 2024 reveals that approximately 29% of localization solutions employ optimization techniques, 48% of which utilize nature-inspired swarm-based algorithms. These algorithms have proven effective for node localization in a variety of applications, including smart cities, seismic exploration, oil and gas reservoir monitoring, assisted living environments, forest monitoring, and battlefield surveillance. This underscores the importance of swarm intelligence algorithms in sensor node localization, prompting a detailed investigation in our study. Additionally, we provide a comparative analysis to elucidate the applicability of these algorithms to various localization challenges. This examination not only helps researchers understand current localization issues within WSNs but also paves the way for enhanced localization precision in the future. Full article

As an important part of global carbon neutrality strategies, carbon dioxide (CO₂) capture, utilization, and storage technologies have emerged as critical solutions for reducing carbon emissions. However, conventional CO₂ applications, including food preservation, industrial synthesis, and enhanced oil recovery, face inherent limitations such as suboptimal gas–liquid mass transfer efficiency and inadequate long-term stability. Recent advancements in CO₂ micro-nanobubbles (CO₂ MNBs) have demonstrated remarkable potential across multidisciplinary domains, owing to their distinctive physicochemical characteristics encompassing elevated internal pressure, augmented specific surface area, exceptional stability, etc. In this review, we try to comprehensively explore the unique physicochemical properties of CO₂ MNBs and their emerging applications, including industrial, agricultural, environmental, and energy fields. Furthermore, we provide a prospective analysis of how these minuscule bubbles can emerge as pivotal in future technological innovations. We also offer novel insights and directions for research and applications across related fields. Finally, we engage in predicting their future development trends as a promising technological pathway for advancing carbon neutrality objectives. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

The perforating–fracturing–testing combined technology has emerged as a crucial well completion technique to enhance production efficiency. However, the shock loads generated during perforation in the packed section of an oil and gas well significantly affect the stability of the perforating tubing string system, potentially leading to deformation or even fracture. During the perforating operation, a large amount of blast products is generated, and as these products escape the perforating gun and interact with the perforating fluid, the fluid pressure pulsates. These pressure fluctuations are the primary cause of the dynamic response of the perforating tubing string. The greatest threat to tubing string integrity occurs when pulsating pressure reaches its peak amplitude, potentially leading to tubing failure. To address this, this study employs underwater explosion theory to analyze the pressure variations during the generation and propagation of shock waves in perforation operations. Additionally, quantitative numerical simulation analysis reveals key relationships governing peak perforating fluid pressure: peak pressure remains remarkably stable at 370–371 MPa despite variations in perforating fluid viscosity (0–110 cP) or tubing Young’s modulus (100–260 GPa). However, it responds significantly to other parameters: fluid density (1–3 g/cm³) causes a linear increase from 335 MPa to 598 MPa; total charge mass drives a proportional rise from 162 MPa to 388 MPa; detonation interval (0–50 μs) elevates pressure from 268 MPa to 378 MPa; and formation pressure (0–100 MPa) increases it from 315 MPa to 372 MPa. Crucially, peak pressure decreases from 376 MPa to 243 MPa as the explosion space expands (0–5 m³). Furthermore, a nonlinear regression model is developed to predict peak perforating shock loads. The results indicate that residual perforation energy critically impacts tubing string safety. Validated against two field cases, the model achieves nearly 10% error compared to predictions from Pulsfrac (industry-standard perforating shock software), meeting field requirements while providing actionable insights for wellbore integrity and perforating tubing string stability. Full article