Search Results (224)

Addressing issues such as corrosion and the eccentric wear of metal tubing strings, low heating efficiency, and high operation and maintenance costs of lifting systems in heavy-oil extraction, core equipment comprising carbon-fiber-reinforced PEEK（Polyetheretherketone） multi-layer composite continuous tubing has been developed. This equipment integrates an embedded cable-laying system and an intelligent regulation module, establishing a rodless oil-extraction technology system suitable for heavy-oil reservoirs. This article systematically describes the process structure, preparation principle, core characteristics, and key parameters of this composite continuous tubing. By deriving an equivalent thermal-resistance model for the multi-layer structure and an unsteady-state heat-transfer equation, precise regulation of the wellbore temperature field is achieved. Combined with field tests at Well A in Jinghe Oilfield, the tubing’s effectiveness in reducing viscosity, increasing production, saving energy, and extending the operational cycle in heavy-oil extraction is verified. The results show that the carbon-fiber-reinforced PEEK composite continuous tubing possesses characteristics such as high strength, strong corrosion resistance, low friction, and high thermal insulation. When paired with a viscosity–temperature coupling regulation algorithm, the heating efficiency is improved by 40% compared to traditional electric heating rods. The efficiency ranges from 37% to 43% when the formation thermal conductivity fluctuates by ±20%. Field applications have achieved a 230% increase in daily oil production, a 30% reduction in system energy consumption, and an extension of the hot washing cycle to over 180 days. The development of this tubing breaks through the technical bottleneck of traditional metal tubing, providing a new material solution for the efficient and intelligent development of heavy-oil extraction, and has broad promotional value. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

This review summarizes the latest research concerning the horizontal flow of two-phase mixtures with viscosities ranging from 0.2 Pa·s to 6.4 × 10⁴ Pa·s. Although our survey is concerned with Newtonian fluids, a short section is included to briefly discuss certain rheological aspects that should be generally considered. In contrast with previous work reporting on the progress in specific domains (e.g., in the oil and gas, chemical, or geophysical contexts), we seek to provide a comprehensive overview of the methods and results used in different contexts. Accordingly, the scope is widened to encompass a broader range of industrial applications and naturally occurring flows. The interest in high-viscosity flows is motivated by the operational challenges occurring in certain systems, most notably in the oil and gas industry, where the production of heavy and extra-heavy crude oils reduces the margins for a safe and efficient operation. Furthermore, this review underlines the cross-field analogies appearing in a broad range of scales and applications. It emphasizes the fundamental role of viscosity in determining the flow patterns, as experimental evidence suggests that the transition boundaries are largely altered at higher viscosities. Some gaps that could be addressed in future work are briefly discussed. Full article

Early frost during the R6 and R7 maturity stages of soybean (Glycine max L.) usually causes immature (green or semi-green) crops to be harvested. These immature soybean seeds have a shrunken appearance, green tone, and high chlorophyll content in the oil, leading to heavy discounts for farmers at the elevator. Previous lab-scale storage studies have shown that seed color can change under light and warm temperatures; however, light cannot be added to a commercial storage bin. Therefore, this study examined the effect of field drying and storage conditions on immature soybean color and oil quality. Soybean planted in two plots were desiccated at the R6 and R7 maturity stages and then allowed to field dry. The field-dried desiccated soybeans were conditioned to moisture contents (MCs) of 12 and 17% and stored in airtight plastic bags at respective temperatures of 4 °C and 22.5 °C for 24 weeks. Seed color, mold, and oil quality were analyzed at intervals of 0, 4, 8, 16, and 24 weeks. The desiccated R6 seeds’ color “a” value significantly changed during field drying from (−9.75 to +0.19) and (−8.96 to +1.95) for Plot 1 and Plot 2, respectively. This means that the color changed from green to a golden yellow or light greenish-brown color after field drying. The chlorophyll content of the desiccated soybeans after field drying at the two maturity stages for both plots was less than 3 mg kg⁻¹ of oil and was relatively stable throughout storage. During storage, at 17% moisture content and 22.5 °C, mold counts increased significantly for R6, R7, and R8 (frozen) control soybeans between weeks 0 and 4 to 4.36 CFU g⁻¹, 5.93 CFU g⁻¹ and 6.22 CFU g⁻¹, respectively. Peroxide and free fatty acid values were within acceptable limits across all storage temperatures and moisture contents. This study suggests that favorable weather conditions for field drying after an early frost have the potential to improve the color of harvested and stored soybeans, similar to mature soybeans. Full article

Although prior research focused on Baghdad has identified variability in fine particulate matter concentrations (PM_2.5) and their origins, there remains uncertainty in the identification of the relative importance of local and long-range PM_2.5 sources. This study analysed hourly air pollutant concentrations and meteorological data from three monitoring sites over the year 2019 in Baghdad, namely Al-Wazeriya (WZ), Al-Andalus Square (AS), and Al-Saiydiya (SA) sites, to determine the nature of PM_2.5 sources. Multi-stage statistical models were utilised to address inherent data limitations and varying sampling dates caused by limitations on power supplies to monitoring equipment, thus improving the identification of urban particulate sources. Bivariate polar plots, concentration ratios, and conditional bivariate probability function (CBPF) plots were used to identify local sources of PM_2.5. Potential Source Contribution Function (PSCF) and concentration weighted trajectory (CWT) methods were employed for distant and regional source apportionment. Domestic diesel generators are suggested to be the primary local source of PM_2.5 pollutants in Baghdad’s WZ area (categorised as residential with significant traffic volumes). Gasoline- and diesel-fueled motor vehicles significantly contribute to PM_2.5 concentrations in the AS and SA areas, which are commercial areas with the latter having close proximity to motorway sources. Additional impacts result from gas flaring and thermal power plants in these regions. Long-range PM_2.5 transport may be attributed to the combustion of low-quality heavy fuel oils from several potential sources, including Nahrawan brick factories, oil fields, and Al-Musayyab thermal power plants, primarily towards the northeast, east, and southeast of Baghdad. Transboundary contributions to PM_2.5 concentrations in Baghdad were also identified, from industrial sources in western Iran and eastern Syria, as well as dust particulates, and oil and gas production from southwestern Iran’s Khuzestan Province, Kuwait, and the Arabian Gulf. Low to medium wind speeds (1–4 ms⁻¹) were linked with the highest source contributions, suggesting local emission sources to be the most significant contributors to high PM_2.5 at the studied sample locations. Full article

The development of high-viscosity oil fields requires technologies that provide not only the thermal mobilization of oil, but also an economically justified level of production with a high energy intensity of the process. One of the most effective technologies of this type is steam-assisted gravity oil drainage (SAGD), but its practical effectiveness is determined by the combined influence of reservoir geology, heat-transfer parameters, and market conditions. The paper proposes a reduced physics-guided model for the rapid technical and economic screening of SAGD in high-viscosity oil fields. The methodological contribution lies in linking geological screening, steam energy input, useful heat delivered to the reservoir, production response, and operating profit within one interpretable analytical chain suitable for pre-feasibility assessment. The study is based on an extended-scenario thermoeconomic analysis of representative heavy-oil development conditions. It is shown that, in a favorable mode, at a depth of about 400 m, oil viscosity of 15,000 cP, steam consumption of 500 t/day and heat-transfer coefficient of 0.7, the estimated production reaches 513–520 t/day, and the net profit is 20,000–22,000 USD/day. In an unfavorable mode, with a depth of about 1000 m, a viscosity of 20,000 cP, a heat-transfer coefficient of 0.4, and a high steam cost, production decreases to 210–230 t/day, and the economic result becomes negative. It has been established that the cost of steam, heat transfer, and the price of oil have a decisive impact on profitability. Full article

Pin–bushing bearings in heavy-duty construction machinery operating in severe industrial environments are susceptible to accelerated wear, grease degradation, and lubrication failure, yet application-specific guidance for lubricant selection and re-greasing intervals under such conditions remains limited. This study evaluates the combined effects of bushing material (hardened steel, cast bronze, and Cu–Sn alloy), grease type (three commercially used greases with viscosities of 120, 460, and 150 mm²/s at 40 °C), and lubrication interval (8, 12, and 24 h) on grease-condition indicators in a field-operating wheel loader used in slag handling, where surrounding slag temperatures may reach 700–800 °C. A Taguchi L9 orthogonal array was used to define nine experimental configurations, each applied for approximately one week under real operating conditions. Grease samples were characterised using the SKF grease analysis kit based on NLGI consistency grade, base oil release rate, and contamination particle count. All greases showed an increase in NLGI grade from 2 to 3–4 during service, indicating thickening and a possible risk of lubrication channel blockage. Oil release rates decreased by up to 60% in some configurations, indicating reduced base oil mobility during service. When the three grease-condition indicators were evaluated together by Grey Relational Analysis, the combination of steel bushing, type B grease (ISO VG 460, lithium complex with MoS₂), and a 12 h lubrication interval showed the most balanced overall response. These findings provide field-based guidance for grease selection and maintenance scheduling in pin–bushing systems operating under demanding service conditions. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

Heavy oil and extra-heavy oil represent mobility-limited petroleum resources because supramolecular associations of asphaltenes and resins, together with strong interfacial resistance, generate extremely high apparent viscosity. In recent years, nanotechnology has emerged as a promising approach for viscosity management and enhanced oil recovery (EOR). This review critically examines recent advances in nano-assisted viscosity reduction from a reservoir-operational perspective and organizes the literature into two field-relevant categories: metal-based and non-metal nano-systems. Metal-based nanoparticles (NPs) mainly promote catalytic aquathermolysis and related bond-cleavage and hydrogen-transfer reactions under hydrothermal conditions, enabling partial upgrading and persistent viscosity reduction during thermal recovery. In contrast, non-metal nano-systems—particularly silica- and graphene-oxide-derived materials—primarily operate through interfacial and structural regulation mechanisms at low or moderate temperatures. These effects include wettability alteration, interfacial-film stabilization, modification of asphaltene aggregation behavior, and the formation of dispersed-flow regimes such as Pickering-type emulsions that reduce apparent flow resistance in multiphase systems. Beyond summarizing nanomaterial types, this review emphasizes reservoir-scale considerations governing field applicability, including brine stability, NPs transport and retention in porous media, and formulation compatibility. Comparative analysis highlights the distinct operational windows of thermal catalytic nano-systems and cold-production nano-systems, providing a reservoir-oriented framework for designing nano-assisted viscosity-reduction technologies. Full article

Extended-solvent steam-assisted gravity drainage (ES-SAGD) has emerged as a promising advancement over conventional SAGD for improving the efficiency and sustainability of in situ heavy oil and bitumen recovery. By co-injecting light hydrocarbon or alternative solvents with steam, ES-SAGD integrates thermal and compositional mechanisms to reduce viscosity, accelerate chamber development, and reduce steam–oil ratios. This review synthesizes the current state of knowledge on ES-SAGD, encompassing fundamental transport mechanisms, solvent selection and phase behavior, mass transfer dynamics, laboratory and physical modeling studies, numerical simulation approaches, and field-scale operational experiences. Experimental evidence consistently demonstrates substantial mobility enhancement through solvent-induced dilution, while compositional thermal simulations highlight an improved sweep efficiency and reduced energy intensity relative to steam-only processes. Field pilots further validate accelerated early-time production and significant steam savings, though challenges related to solvent retention, asphaltene stability, and reservoir heterogeneity persist. Key research gaps are identified in solvent transport prediction, formation damage risk, long-term solvent recovery, and integrated economic–environmental optimization. Overall, ES-SAGD offers a viable pathway toward lower-emission, higher-efficiency bitumen production, provided that solvent chemistry, reservoir complexity, and operational controls are carefully managed through continued research and targeted field deployment. Full article

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

The rheological behavior of a 9.5 °API extra-heavy dead crude oil produced in the southern Gulf of Mexico is experimentally investigated over the temperature range $30 °\mathrm{C}\le T\le 100 °\mathrm{C}$. Steady-shear measurements are used to characterize the stress–strain-rate response and apparent viscosity under controlled laboratory conditions representative of surface transport. Statistical analyses show that the oil exhibits a Bingham plastic behavior at 30 °C, transitions to a Herschel–Bulkley-type response at 50 °C, and displays a predominantly dilatant behavior at 100 °C. Existing dead oil viscosity correlations commonly used in field applications are evaluated against the experimental data and are found to systematically underpredict the viscosity by approximately one order of magnitude within the studied temperature range. Motivated by the observed exponential dependence of viscosity on temperature, a crude-specific viscosity–temperature correlation is proposed for this specific crude oil. The new correlation provides a significantly improved representation of the experimental data and leads to substantially more accurate pressure drop predictions in a representative pipeline transport scenario. The results highlight the importance of crude-oil-specific rheological characterization and viscosity modeling for reliable flow assurance analyses involving extra-heavy crude oils. Full article

The decision-making procedure to develop and manage a production strategy is challenging because it requires a high investment and is performed under uncertainty. Heavy-oil reservoirs present low mobility and a high production of water under waterflooding. However, intelligent wells with ICVs (inflow control valves) and polymer flooding can improve the field’s performance. This work proposes a decision analysis to select the best strategy for the development of a heavy-oil field, evaluating and comparing the feasibility of waterflooding, polymers, and ICVs. We complement the nominal optimization accomplished for the base case in previous works by considering a probabilistic procedure with uncertainties, which includes the following: the generation of uncertain scenarios, the initial risk evaluation, the optimization of production strategies, a risk curve analysis, and the selection of the best strategy. A model-based reservoir simulation is used to perform the procedure, with the Expected Monetary Value (EMV) quantifying the economic returns. The case study is a sandstone heavy-oil reservoir (13° API) that represents a real Brazilian offshore field. Based on the EMV, we selected the polymer flooding strategy for this case study. However, since better water management was achieved with small differences to the polymer strategy, the option of using the ICVs in combination with polymer could be attractive depending on the various objectives of an oil field. Full article

Wood remains a cornerstone material in construction and outdoor applications, yet its durability is continually compromised by fungal decay and insect infestation. Increasing regulatory restrictions on conventional wood preservatives and growing sustainability demands have intensified interest in bio-based alternatives. Among these, essential oils exhibit strong antifungal and insect-repellent activity but suffer from high volatility, leaching, and limited durability under moisture exposure. This review examines recent advances in chitosan nanoparticle-based encapsulation of essential oils as a strategy to overcome these limitations and enable more sustainable and environmentally responsible wood protection systems. The review synthesizes current knowledge on nanoparticle synthesis routes, physicochemical properties, bioactive delivery mechanisms, antifungal and anti-termite performance, and behavior under moisture and weathering conditions, alongside sustainability and regulatory considerations. The reviewed literature demonstrates that chitosan nanoparticles enhance essential oil retention, stability, and controlled release, leading to improved resistance against biological deterioration compared with unencapsulated formulations. In addition to performance benefits, these nano-enabled systems align with circular bioeconomy principles by utilizing renewable and waste-derived feedstocks while avoiding heavy metals and persistent synthetic biocides. Despite promising laboratory results, challenges remain related to long-term field performance, scalability, and environmental fate. Overall, chitosan–essential oil nano-formulations represent a versatile platform for next-generation, low-hazard wood protection, offering a promising pathway toward sustainable and durable wood preservation technologies. Full article