Search Results (93)

Functionally graded materials (FGMs) have the potential to revolutionize the oil and gas transportation sector, due to their increased strengths and efficiencies as pipelines. Conventional pipelines frequently face serious problems such as extreme weather, pressure changes, corrosion, and stress-induced pipe bursts. By analyzing the mechanical and thermal performance of FGM-based pipes under various operating conditions, this study investigates the possibility of using them as a more reliable substitute. In the current study, the post-buckling and nonlinear vibration behaviors of pipes composed of FGMs transporting heavy crude oil were examined using a Timoshenko beam framework. The material properties of the FGM pipe were observed to change gradually across the thickness, following a power-law distribution, and were influenced by temperature variations. In this regard, two types of FGM pipes are considered: one with a metal-rich inner surface and ceramic-rich outer surface, and the other with a reverse configuration featuring metal on the outside and ceramic on the inside. The nonlinear governing equations (NGEs) describing the system’s nonlinear dynamic response were formulated by considering nonlinear strain terms through the von Kármán assumptions and employing Hamilton’s principle. These equations were then discretized using Galerkin’s method to facilitate the analytical investigation. The Runge–Kutta method was employed to address the nonlinear vibration problem. It is concluded that, compared with pipelines made from conventional materials, those constructed with FGMs exhibit enhanced thermal resistance and improved mechanical strength. Full article

A pipeline network is an important transportation mode of natural gas, and different external factors will affect the development of natural gas scheduling plans to different degrees. However, the specific correlation between each external environmental factor and pipeline network scheduling decision is not clear at this stage. This paper developed a hybrid method with Pearson’s correlation coefficient and Spearman’s correlation coefficient to study the correlations between climate temperature, total gas supply, economic conditions, other energy consumption and natural gas pipeline scheduling plans. The results showed that the correlation between natural gas pipeline output and climate temperature is good, presenting a significance level of 5% and below; in contrast, the correlations with economic conditions and other factors are less significant but still reach a significance level of 10%. Meanwhile, taking energy consumption as the object of study, it was found that the correlation between natural gas consumption and electric energy, crude oil and crude coal is good, showing a significance level of 5% and below. Among them, there is a significant positive correlation between natural gas consumption and electric energy consumption, and between natural gas consumption and crude oil consumption, which reveals the synergistic effects within the energy system. Full article

Engineering disturbances are increasing in permafrost regions of northeastern China, where soil microorganisms play essential roles in biogeochemical cycling and are highly sensitive to linear infrastructure disturbances. However, limited research has addressed how microbial communities respond to different post-engineering-disturbance recovery stages. This study investigated the impacts of the China–Russia Crude Oil Pipelines (CRCOPs) on soil microbial communities in a typical boreal forest permafrost zone of the Da Xing’anling Mountains. Soil samples were collected from undisturbed forest (the control, CK); short-term disturbed sites associated with Pipeline II, which was constructed in 2018 (SD); and long-term disturbed sites associated with Pipeline I, which was constructed in 2011 (LD). Pipeline engineering disturbances significantly increased soil clay content and pH while reducing soil water content (SWC), soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) (p < 0.05). No significant differences in these soil properties were observed between SD and LD. Bacterial diversity increased significantly, whereas fungal diversity significantly decreased following pipeline disturbances (p < 0.05). The beta diversity of both bacterial and fungal communities differed significantly among the three disturbance types. At the phylum level, pipeline disturbance increased the relative abundances of Proteobacteria, Acidobacteriota, Actinobacteriota, Ascomycota, and Mortierellomycota while reducing those of Bacteroidota and Basidiomycota. These shifts were associated with disturbance-induced changes in soil properties. Microbial co-occurrence networks in SD exhibited greater complexity and connectivity than those in CK and LD, suggesting intensified biotic interactions and active ecological reassembly during the early recovery phase. These findings suggest that pipeline disturbance could drive soil microbial systems into a new stable state that is difficult to restore over the long term, highlighting the profound impacts of linear infrastructure on microbial ecological functions in cold regions. This study provides a scientific basis for ecological restoration and biodiversity conservation in permafrost-affected areas. Full article

To address the lack of theoretical understanding regarding crude oil wall adhesion in non-metallic pipelines and to compare it with the behavior in metallic pipelines, this study investigates the wall adhesion behavior of crude oil in non-metallic pipelines using a self-developed, simulated stirred tank setup. The main factors influencing crude oil adhesion in non-metallic pipelines were identified, and the differences in adhesion behaviors across different pipeline materials were clarified. Additionally, the reasons behind these behavioral differences were explored through contact angle measurements and the interfacial energy theory. The results indicate that the factors affecting the transportation temperature of crude oil in non-metallic pipelines include the overall water content, shear strength, and wax content of crude oil. On average, the amount of adhered crude oil in the three types of non-metallic pipeline was lower than that in the metallic pipelines. Among them, the flexible, composite non-metallic pipeline showed the greatest reduction in average adhered oil mass by 22.86%. This significant reduction is attributed to the reduced adhesion of crude oil on the non-metallic surfaces. Therefore, using non-metallic pipelines in crude oil transportation networks is advantageous for implementing low-temperature gathering and transportation processes. Full article

During the pipeline transport of high-wax crude oil, paraffin precipitation often results in deposit formation, and a thorough investigation into the issue of wax deposition is crucial for ensuring the safe operation and economic benefits of the pipeline. This work critically reviews the latest research progress focusing on the mechanisms, the factors influencing it, and the kinetic models used to describe it. Although research on single-phase crude oil wax deposition has made certain progress both domestically and internationally, existing studies have limitations in terms of the diversity of crude oil types. In previous studies, the types of crude oil used to explore influencing factors were relatively singular. When modeling, the diversity of crude oil properties was not fully considered, leading to a lack of general applicability of the established models across different types of crude oil. To overcome this limitation, future research should place greater emphasis on the diversity of crude oil properties. Specifically, it is necessary to collect and analyze wax deposition data from a wider variety of crude oils and delve into the mechanisms by which different crude oil properties influence the wax deposition process. Therefore, future research needs to further take into account the diversity of crude oil properties and establish kinetic models that are quantitatively correlated with these properties. This will contribute to more accurate prediction and assessment of wax deposition risks during pipeline transportation for various types of crude oil, thereby providing robust assurance for the safe operation of pipelines. Full article

Experiences in the production, transportation and preparation of crude oil for transportation have shown that specific problems arise related to their mixing, including water contamination. In recent years, interest in studying these problems has significantly increased, mainly due to the development of extraction technologies for heavy oil samples and bitumen. Along with various difficulties encountered during the pipeline transportation of complex rheological crude oil blended with each other and with light oil, including condensate (such as sedimentation, etc.), imbalances are also observed during storage, as well as in the processes of delivery and reception. During the dehydration of oil mixtures, a synergistic effect is observed in the consumption of demulsifier. The article investigates, in accordance with international standards and based on laboratory tests, how the physico-chemical properties (density, viscosity, freezing point, saturated vapor pressure, chemical composition) of mixtures formed by blending various grades and compositions of Azerbaijani oil examples with each other and with condensate change and how the efficiency of dehydration of oil mixtures is affected by the mixing ratio of the oil involved. It was found that the quality indicators (physico-chemical parameters) of oil mixtures differ non-additively from the initial parameters of the blended products and in some cases, this difference is even observed with anomalies. Moreover, depending on the mixing ratio of the oil, variations in the consumption of demulsifier were also identified. Full article

Currently, most crude oil viscosity correlations rely only on API gravity and temperature, resulting in significant limitations in accurately predicting crude oil viscosity. To address this issue, this paper systematically explores the effect of API gravity, and crude oil components (saturates, aromatics, resins, and asphaltenes content), on viscosity based on 251 crude oil samples through sensitivity analysis. To overcome the shortcoming of traditional models, this paper proposes an innovative generalized viscosity model that combines the Levenberg–Marquardt (LM) and universal global optimization (UGO) methods to fully consider the effects of API gravity and various crude oil components. To verify the effectiveness of the model, this paper divides the 251 crude oil samples into a training set (202 samples) and a test set (49 samples) and compares the prediction results of the new model with the traditional. The results show that the prediction accuracy of the new model on the training set and test set is significantly better than that of the traditional model, with the minimum average absolute relative deviation reaching 10.13% and 12.4%, respectively. This study not only improves the accuracy of crude oil viscosity prediction but also provides early warning of increased pipeline friction caused by abnormal viscosity, avoids suspension accidents, and ensures the safe operation of long-distance pipelines. Full article

With the continuous growth of global energy demand, the exploitation of deepwater oil and gas resources has become an important part of national energy strategies. The high-viscosity crude oil in deepwater areas such as the South China Sea poses severe challenges to oil and gas pipeline transportation due to its high pour point and high viscosity characteristics. Wax deposition, particularly significant under low temperature and high viscosity conditions, can lead to reduced pipeline flow rates, decreased transportation efficiency, and even potential safety hazards. Therefore, in-depth research on the wax deposition characteristics and mechanisms in high-viscosity systems holds significant theoretical and engineering application value. Current research primarily focuses on the influencing factors of wax deposition, deposition mechanisms, and the establishment of prediction models. Studies have shown that external factors such as temperature, shear intensity, operating time, and water content have significant effects on the wax deposition process. Specifically, increased temperature differences accelerate the deposition of wax molecules, while the presence of the aqueous phase inhibits wax crystallization and deposition. Furthermore, the formation mechanisms of wax deposition mainly include molecular diffusion, shear stripping, and aging effects. Researchers have explored the dynamic changes and influencing laws of wax deposition by establishing mathematical models combined with experimental data. In summary, although some progress has been made in studying the wax deposition characteristics in high-viscosity systems, research on wax deposition characteristics in mixtures, especially under the combined action of pour point depressants and flow improvers, is still inadequate. Future research should strengthen the systematic exploration of wax deposition mechanisms, quantify the effects of different external factors, and develop wax deposition prediction models suitable for practical engineering to ensure the safe and stable operation of deepwater oil and gas pipelines. Through in depth theoretical and experimental research, robust technical support can be provided for the efficient development of deepwater oil and gas resources. Full article

Using machine learning to predict maintenance schedules for crude oil pipelines is crucial for enhancing efficiency and minimizing disruptions in the oil and gas sector. Our research explores the effectiveness of machine learning algorithms in this context, with a specific focus on using oil flow rate as a primary predictor. When trained with a variety of inspection data, machine learning models can accurately predict flow rates, thus improving maintenance planning. Several pipeline scenarios were analyzed, and the Python library was used for dataset augmentation. The study shows a correlation between variations in the buildup deposits and the oil flow rate in the pipeline, indicating that the oil flow rate gives an indication for determining the need for maintenance. The flow rate was categorized into three efficiency levels: High Efficiency (flow rate > 90% of the allowable rate), Moderate Efficiency (flow rate between 70% and 90%), and Low Efficiency (flow rate < 70%). Each efficiency level was linked to specific maintenance intervals: Specifically, a higher flow rate allowed longer intervals between maintenance activities, while a lower flow rate could indicate there is an accumulation of deposits that necessitates urgent intervention. Several machine learning models were trained, and variations in performance were observed. Gradient Boosting and XGBoost Regressor show the best performers with lower values for MSE, RMSE, and MAE and higher R² scores compared to the Support Vector Regressor. The result shows Gradient Boosting has an MSE of 0.000005, RMSE of 0.002259, MAE of 0.000968, and an R² of 0.997259, followed by XGBoost Regressor with MSE of 0.000005, an RMSE of 0.002269, an MAE of 0.000922, and an R² of 0.997234. Support Vector Regressor indicates the least performance, with an MSE of 0.002868, RMSE of 0.053554, MAE of 0.046311, and an R² of −0.540765. The findings of the study emphasize the necessity of choosing machine learning algorithms that are appropriately suited to the features of the dataset and the task. The findings highlight the importance of selecting machine learning algorithms that are more suitable to the features of the dataset and the task. Full article

The flow management of the gas–liquid mixture module is crucial for the transmission efficiency of crude oil-and-natural gas-gathering and transportation systems. The concurrent flow of high-viscosity crude oil and natural gas in gas–liquid mixing is investigated numerically by adopting an improved volume of fluid (VOF) model programmed with the OpenFOAM v2012 software package. Over a wide range of superficial velocities for the oil, from 0.166 to 5.529 m/s, and natural gas, from 0.138 to 27.645 m/s, a variety of flow regimes of bubble flow, plug flow, slug flow, and annular flow are encountered successively, which are essentially consistent with the Brill and Mandhane flow regime identification criteria. The results show that the oil volume fraction, fluid velocity, and bubble slip velocity together affect the growth of bubbles in the pipeline at a low gas velocity. In the case of slug flow, the phenomenon of liquid film plugging is noticeable, and the flow is very unstable, which should be avoided as much as possible. Nonetheless, it is commended that stable plug flow and annular flow with a high oil transportation efficiency and minimal power consumption are friendly working conditions. Full article

To address the challenges and risks associated with the declining crude yield, an optimization project for the surface production facilities at ZY Oilfield is underway. Upon the completion of this project, the oilfield’s export pipelines will transport water-containing live crude oil. To ensure pipeline transportation safety, it is essential to clarify the phase behaviors and flow state of water-containing live oil. For this purpose, the VLLE characteristics of water-containing live oil were analyzed with Aspen HYSYS V12 software and validated through PVT tests. Additionally, the pressure variations in multiphase flow pipelines under different operating conditions were calculated using the Beggs and Brill–Moody–Eaton method with Pipephase 9.6 software. The results indicated that the bubble point pressure and vapor fraction of water-containing live oil were higher than those of dehydrated dead crude within the operating temperature range. Liquid–gas flow was likely to occur in the presence of low soil temperatures, low oil output, low outlet pressure, high outlet temperatures, or small water fractions, particularly at the pipeline ends. Moreover, the optimized technological processes for stations and pipeline operations were proposed. The findings offer a new approach for the safe transportation of low-output live oil and provide valuable insights for optimizing surface production in aging oilfields. Full article

Carbon Capture, Utilization and Storage (CCUS) technology is recognized as a pivotal strategy to mitigate global climate change. The CO₂ storage and enhanced oil recovery (CCUS-EOR) technology not only enhances oil recovery rates but also contributes to significant reductions in CO₂ emissions, with significant social and economic benefits. This paper examines the application of CO₂-EOR technology in both enhancing oil recovery and facilitating geological CO₂ storage, and analyzes its implementation status and differences in the United States and China. Through experimental investigations conducted in a specific oilfield, we analyze the effects of dissolved CO₂ on the viscosity–temperature characteristics, yield value under pressure, stability, and rheological properties of crude oil and produced fluids. Additionally, we assess the demulsification effectiveness of various demulsifiers. Our findings indicate that both dissolved CO₂ in crude oil and emulsions exhibit non-Newtonian fluid behavior characterized by shear thinning, and the viscosity decreases with the increase in temperature and pressure. Furthermore, the presence of dissolved CO₂ exacerbates the oil–water separation phenomenon in produced fluids, thereby diminishing emulsion stability. The increase in emulsion concentration and the increase in emulsification temperature are both conducive to improving the emulsification rate. These research results provide critical insights for pipeline design and pump selection in oilfield production processes. Full article

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km² region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta. Full article

In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium. Full article

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions. Full article