Oil Temperature Research Articles

Leakage prediction approach and influencing factor analysis from seal test

Purpose Leakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions. Design/methodology/approach Seal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated. Findings The results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence. Originality/value This work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

REDUCING THE VISCOSITY OF THE OIL MIXTURE BY A ROTARY PULSE APPARATUS IN UKHTA — YAROSLAVL OIL PIPELINE

Physical methods of exposure can be used to improve the rheological characteristics of viscous, resinous oils, the transportation of which through main pipelines requires additional operating costs. The authors of this article evaluated the effectiveness of the method for improving oil rheology due to multifactorial effects (shear loads, high–amplitude pressure pulses, developed turbulence) on a sample of a mixture of oils of the Ukhta - Yaroslavl oil pipeline in a rotary pulse apparatus (RIA). The specific power consumption for processing an oil sample was 2.84 kWh/m3. The initial rheological and physico-chemical parameters of the studied oil are described, according to which it is characterized as heavy, viscous, highly resinous oil. The analysis of the flow curves showed that, by the nature of the flow, the sample of a mixture of oils from the Ukhta — Yaroslavl main oil pipeline belongs to Newtonian liquids. The evaluation of the effectiveness of the multifactorial effect on the oil sample implemented in RIA was carried out using dimensionless coefficients showing the ratio of dynamic viscosity, the specific power required to maintain the flow of oil in a rotary viscometer before and after physical exposure to oil, as well as the ratio of the change in the energy of thixotropy of oil to the energy spent on the processing process. The samples were processed in an RIA-based installation, in which pressure pulsations, developed turbulence, and large shear loads are generated in the liquid flow. When comparing the viscosity values of untreated and treated mixed oil in a rotary pulse apparatus, the dynamic viscosity measured at 20 °C decreased by 8 %. The component composition of the oil after processing in RIA has practically not changed. Measuring the viscosity of oil before processing and immediately after processing in RIA shows a decrease in viscosity by more than two times, since the temperature of the oil flow increases after passing through RIA by more than 10 °C. The increase in oil temperature during processing is due to large shear stresses during its flow in slit channels and in the gap between the rotor and stator due to the large amplitude of pressure pulsations and flow velocity.The relaxation time of the rheological parameters of the treated oil was seven days.

Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

Particulate emissions from cooking: emission factors, emission dynamics, and mass spectrometric analysis for different cooking methods

Abstract. Since most people, especially in developed countries, spend most of their time indoors, they are heavily exposed to indoor aerosols, which can potentially lead to adverse health effects. A major source of indoor aerosols are cooking activities, which release large quantities of particulate emissions (in terms of both number and mass), often with complex compositions. To investigate the characteristics of cooking emissions and what influences these emissions, we conducted a comprehensive study by cooking 19 dishes with different ingredients and cooking methods. The emissions were monitored in real time with several online instruments that measured both physical and chemical particle properties as well as trace gas concentrations. The same instrumentation was used to study the influence of cooking emissions on the ambient aerosol load at two German Christmas markets. In contrast to previous studies, which often focus on individual aspects or emission variables, this broad and coherent approach allows a comparison of the influence of different parameters (e.g., ingredients, cooking method, cooking temperature, cooking activities) on the emissions. We found an influence of cooking emissions on six variables: number concentration of smaller (particle diameter dp > 5 nm) and larger (dp > 250 nm) particles, particulate matter (PM: PM1, PM2.5, PM10), black carbon (BC), PAHs (polycyclic aromatic hydrocarbons), and organic aerosol mass concentration. In general, similar emission characteristics were observed for dishes with the same cooking method, mainly due to similar cooking temperature and use of oil. The temporal dynamics in the emissions of the aforementioned variables, as well as the sizes of the emitted particles, were mainly influenced by the cooking temperature and the activities during cooking. Emissions were quantified using emission factors, with the highest values for grilled dishes, 1 to 2 orders of magnitude lower for oil-based cooking (baking, stir-frying, deep-frying), and the lowest for boiled dishes. For the identification of cooking emissions with the Aerodyne aerosol mass spectrometer (AMS), and more generally for the identification of new AMS markers for individual organic aerosol types, we propose a new plot type that takes into account the mass spectral variability for individual aerosol types. Combining our results and those of previous studies for the quantification of cooking-related organic aerosols with the AMS, we recommend the use of relative ionization efficiency values higher than the default value for organics (RIEOrg = 1.4): 2.17 ± 0.48 for rapeseed-oil-based cooking and 5.16 ± 0.77 for soybean-oil-based cooking.

Investigation of the Dynamic Relationship between Oil Temperature and Bearing Gearbox Condition Indicator Values for the Bell 407 Helicopter Based on Cointegration Analysis

The primary objective of this study is to investigate the dynamic relationship between oil temperature and the Bearing Gearbox Condition Indicator (BGCI) values of the Bell 407 helicopter. To achieve this goal, we employ robust econometric tools, such as unit root tests, cointegration tests, and Autoregressive Distributed Lag (ARDL) models for both, long-run and short-run estimates. Our findings indicate that variable temperature tends to converge to its long-run equilibrium path in response to changes in other variables. The results of the ARDL analysis confirm that spectral kurtosis, inner race, cage, and ball energy significantly contribute to the increase in temperature. Furthermore, we use the impulse response function (IRF) to trace the dynamic response paths of shocks to the system. The identification of a cointegrating relationship between oil temperature and BGCI values suggests a practical and significant connection that can potentially be used to predict hazardous changes in oil temperature using BGCI values, which is an important implication for enhancing the safety and reliability of helicopter operations. This study presents a promising direction for condition monitoring (CM) in rotating aircraft machinery, emphasizing the potential of integrating temperature data to simplify the diagnostic process while still achieving reliable results.

Enhancing wind turbine energy efficiency: Tribo-dynamics modeling and shape modification

The performance of wind turbine gearbox planetary bearing systems directly influences the turbine's reliability, energy production efficiency, and economy. However, the lack of a comprehensive model to accurately predict the dynamic meshing load of helical gears and temperature effects hinders a deeper understanding of the transient performance of gearbox planetary sliding bearings. Additionally, research on shape modification for gearbox planetary sliding bearings remains inadequate. This study develops a comprehensive tribo-dynamics model that considers the dynamic meshing forces of helical gears and temperature effects. The model's effectiveness is validated by the concordance between experimental data from a full-size test bench and simulation results on oil film thickness and temperature. Transient simulation analysis indicates insufficient lubrication, high-temperature rise, and severe contact that reduce electricity production efficiency and reliability, all of which occur at bearing axial edges. Therefore, three modified bearing designs (super-elliptical, trapezoidal, and quadratic shapes) are introduced, and their improved performance is thoroughly compared. The super-elliptical design increases the minimum oil film thickness by 2.60 μm. The trapezoidal design reduces cumulative friction energy losses by 4.34 %. Each modified design successfully reduces the edge wear and oil temperature. The underlying enhancing mechanisms are revealed to be oil film uniform redistribution and lubrication states' alteration. This work can contribute to the reliability, electricity production efficiency, and economy of global wind turbines and support the achievement of net-zero emission goals.

Comparison of measured and calculated high-viscosity crude oil temperature values in a pipeline during continuous pumping and shutdown modes

The paper investigates the stationary and non-stationary cooling processes of high-viscosity crude oil temperature in the Severnye Buzachi – Karazhanbas pipeline during both pump operations and pump shutdowns. To transport high-viscosity crude oil through pipelines, the hot pumping method is employed. During oil transportation and pump shutdowns, the oil temperature decreases due to heat exchange with the environment, leading to an increase in oil viscosity. As viscosity rises, more power is required from the pumps to move the thickened oil, resulting in higher electricity consumption. To avoid increased operational costs after pump shutdowns, it is crucial to accurately calculate the oil cooling temperature in order to determine the safe shutdown duration and a critical temperature threshold. The Shukhov formula is used to calculate oil temperature during transportation, while a modified version is applied during pump shutdowns. To verify the accuracy of the obtained oil temperature results, the calculated values were compared with the measured values from pipeline sensors in both stationary and non-stationary cases. The novelty of the work is in determining the methodology for calculating the safe shutdown time of a pipeline for high-viscosity oil. The proposed formula was validated using experimental data from SCADA system sensors in real-time.

Automation of the Oil Extraction Process Performed by Means of A Screw Press

Abstract The continuous development of the oil-manufacturing industries causes the necessity of improving extraction technologies. In this case, the specific interest is focused on the control systems of screw presses. Among a great variety of such machines, the small household presses are in significant demand among consumers. Various seeds and kernels require different technological conditions to be provided in order to maximize the qualitative and quantitative characteristics of the extracted oil. Therefore, the main objective of this research is developing and testing the control system allowing for automation of the oil extraction process. Particularly, the temperature parameters of the pressing chamber, extracted oil, and electric motor are to be monitored and limited. In addition, the consumer should be able to predefine the mass of the oil to be extracted. Considering the small household screw press LiangTai LTP200, the general algorithm (block diagram) of the control system operation is proposed and the corresponding experimental prototype is developed. The latter is based on the Arduino Mega microcontroller and is equipped with three temperature sensors, two coolers (fans), one heater, and one mass sensor. The proposed control system allows for continuous monitoring and limiting of the pressing chamber, oil, and electric motor temperatures, as well as the mass of the extracted oil. The experimental data show that the pressing chamber preheating process lasts for about 3 min (170…190 s) and its maximal temperature does not exceed 44°C. The temperature of the extracted oil does not rise over 61°C. The motor temperature changes within the range of 69...71°C. The oil extraction productivity is as follows: 1.2 kg/h (sunflower seeds), 1.06 kg/h (walnut kernels), 0.9 kg/h (almond kernels), and 0.78 kg/h (peanut kernels). The obtained results can used in further investigations focused on analyzing the influence of these parameters on the quantitative and qualitative characteristics of the extracted oil.

Investigation on the impact of CO2-Induced precipitation on microscopic pore structure of low-permeable reservoirs

CO2 injection into reservoirs might induce organic or inorganic precipitation in microscopic pores, and some of the pores are blocked by the precipitates, resulting in a decrease in the connectivity of pores and affecting oil recovery. In this paper, a set of experiments on CO2 displacement and CO2-water-oil-rock interaction are conducted to study the microscopic mechanisms of reservoir damage. In-situ Nuclear Magnetic Resonance (NMR) technology is applied to conduct the microscopic analysis of CO2-EOR and reservoir damage at real reservoir conditions. The results reveal that the particle size and the amount of inorganic precipitates formed in the formation water will increase with the rise of CO2 injection pressure, and the inorganic precipitates primarily consist of siderite and kaolinite. The change of NMR T2 spectra of the core samples reveals that the inorganic precipitates primarily block the small pores less than 0.89 μm. However, as a result of stronger mineral dissolution and solute transport, the pore volume of the large pores (greater than 0.89 μm) increases after the CO2-water-rock reaction. Ultimately, this interaction results in a 5.4% increment in pore volume. In addition, the displacement efficiency of water-saturated cores is improved after the first CO2 displacement, which indicates that CO2-water-rock interaction can effectively improve the seepage properties of reservoirs. Nevertheless, the reservoir damage caused by asphaltene precipitation during CO2 flooding is more significant. The asphaltene precipitation percentage in formation oil increases with the rise of CO2 injection pressure, reservoir temperature, and asphaltene content in virgin oil. CO2-induced asphaltene precipitation causes more severe blockage on small pores in comparison with large pores. After 2 h of CO2 injection to the oil-saturated core, permeability and oil recovery caused by CO2-induced precipitation decreased by 17.6% and 11.4%, respectively.

Oil recovery and cooling for underground salt cavern oil storage: Insights from coupled flow and thermal model

Petroleum reserve in salt caverns is paying more attention to oil cooling to prevent dissolved gas release from heated crude oil. This work introduced a method for oil recovery in conjunction with cooling, along with a matching flow and heat transfer model taking changes in crude oil thermophysical parameters into consideration. An average error of 3.43% was observed between the model results and filed monitoring data. It was found that after being cooled by the heat exchanger, the temperature of extracted crude oil drops from 39.5 °C to 26.8 °C, demonstrating the effectiveness of this approach for oil cooling. Sensitivity analysis indicates that the season is the primary determining factor for the cooling efficacy of crude oil. Even worse, cooling will be useless when the ambient temperature exceeds 35 °C. However, increasing brine temperature will reduce the surface pumping pressure from 7.60 MPa to 5.17 MPa, with a decrease of up to 31.97%. A special focus was given to the brine injection rate, which seemed to have less impact on the cooling efficiency but became more significant in the pressure profile. In addition to providing further insights into the flow and thermal performance of strategic petroleum reserves in the salt cavern, this study could also be a powerful tool for determining the best process parameters to ensure the delivery temperature of crude oil and the stability of the salt cavern.

Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.

A study into the fluid power control characteristics of a hydrostatic transmission for mobile machinery

In response to improve the quality and level of hydrostatic transmission (HST) products for modern mobile machineries, the closed-loop transfer functions of a HST are established by mathematical modeling, a comprehensive testing equipment is developed employing a proportional relief valve for automatic program loading and fuzzy-PID oil temperature control, the testing equipment is capable of conducting various performance tests for various models of HST integrated pump-motor products, including factory and type tests, especially continuous impact and endurance performance tests under high temperature and high-speed conditions. Comprehensive performance test of the above test sample is carried out using the developed equipment, that the test results agree well with the previous simulation results verifies the correctness of the established theoretical analysis model. The obtained theory and equipment from this work provide theoretical and experimental technical support for improving design and development of high-end fluid components for modern mobile machinery HST.

Numerical study on drag reduction and heat collection efficiency of oil based on solar concentrating system

Traditional onshore crude oil storage and transportation consumes a lot of energy. In this paper, a concentrating type of high-temperature crude oil transportation pipeline heating system is proposed, which uses concentrators to collect and focus sunlight on the pipeline. The high-temperature oil flows through the heat exchanger at the end of the pipeline, where it exchanges heat with water to store and recover heat for utilization in the heat collector. The temperature rise is significant and the uniformity improves as oil extends through the pipeline under various multiples of solar radiation achieved with concentrators. The inlet rate of oil is a negative factor affecting the outlet temperature. Compared to non-concentrated light, the friction coefficient and viscosity of oil are reduced, resulting in a more effective drag reduction effect. To validate the accuracy of simulation results and compare them with theoretical calculations, ensuring that the margin of error is within 8.5%. The type of concentrators, laying distance of the concentrators below the pipeline, concentration ratio (CR), and the inlet rate can be adjusted based on climatic conditions and required oil temperature. This paper is of great significance for reducing energy consumption in long-distance oil transportation and realizing the energy transition.

Simulation method for dynamic characteristics of rotor-support system subjected to environmental loads

An improved finite element method was proposed to investigate the dynamic characteristics of rotor system with environmental loads (temperature and aerodynamic forces) in aeroengines. According to the axial temperature distribution within shaft elements, the polynomials were established for physical properties of material. The derivation for coefficient matrices under thermal loads, and the tensile stiffness matrix considering axial aerodynamic forces were completed, based on the Timoshenko beam theory. A high-pressure rotor was simulated with operating temperature 200∼600∘C, and the magnitude of aerodynamic force is 105N. Furthermore, a squeezed film damper (SFD) was adopted to this rotor-support system, its steady responses were solved by the equivalent coefficient method, and the Newmark-HHT method was utilized to solve the transient responses. The results indicate that the first two critical speeds are reduced by 1.85 % and 2.07 %, which is mainly caused by thermal loads. The rising temperature leads to lower elastic modulus, higher Poisson's ratio and expansion ratio. With the amount of a mass unbalance being 500 g·mm located at the 1st-stage compressor disk (CD1): the 1st resonance peaks at CD1 and turbine disk (TD) increase by 4.56 % and 4.98 %, respectively, and the 2nd resonance peaks at CD1 and TD rise 5.88 % and 5.00 %, separately. Owing to SFD damping, the first two resonance peaks at CD1 and TD decrease by 69 % and 51 %, respectively. Compared to 40 °C, the first two resonance peaks at CD1 increase by 88 % and 55 % at the oil temperature 100 °C. The oil-film damping deteriorates significantly due to the lower oil viscosity in higher temperature. When operating near the first-order critical speed, the transient responses of rotor perform as simple harmonics, circular precession trajectory, dominant rotational frequency, and periodic motions. Overall, the dynamic characteristics of rotor-SFD-support systems exposed to environmental loads can be predicted by the proposed simulation method.

Fusion of Image Transformation Techniques for IoT-based Multivariate Time-Series

The emergence of the Internet of Things (IoT) has ushered in a new era of data generation, with the opportunity for data to become a key element of connected devices. This study investigates new methods to bridge the realms of multivariate time-series data and image analysis, paying special attention to Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP) transformation techniques. These techniques serve to convert raw time-series data into visual representations, laying the foundation for deeper analysis and predictive modeling. The study introduces a novel paradigm by not only employing individual image transformation techniques but also fusing them in both horizontal and square orientations. By leveraging Convolutional Neural Networks (CNNs), this study demonstrates the efficiency of innovative fused oriented image transformation techniques in predicting complex patterns within a multivariate time-series dataset related to electricity distribution and transformer oil temperature. Experimental results demonstrate that the fusion of image transformation techniques yields notably enhanced performance compared to employing each technique individually. Fused-Horizontal techniques exhibit the highest performance, while Fused-Square techniques show the second best performance among all fused oriented and individual techniques. This emphasizes the importance of fused oriented image transformation techniques in prediction tasks.

Research on input parameter optimization for NOx deep learning prediction model

Optimizing the input parameters for NOx prediction model is instrumental in enhancing the precision and efficacy of the prediction model. This paper focuses on the input variables of the data-driven diesel engine NOx prediction model. According to the diesel engine NOx generation mechanism, at first relevant variables are selected, then the similarity method and parameter sensitivity method are compared to optimize the input variables. Firstly, this paper conducted a real-road emission test of a heavy-duty diesel vehicle, and proposed a data-driven deep learning model for predicting diesel engine NOx emissions based on the experimental data. The experimental data were processed using an Attention-Mechanism based Convolutional Neural Networks-Long Short-Term Memory (AM-CNN-LSTM) model. Subsequently, input parameters were selected according to the sensitivity weights assigned to each parameter in the preliminary model. This approach refined the modeling data and excluded variables not pertinent to NOx emission prediction. The AM-CNN-LSTM model prediction performance has been improved with 2% increase of model R2 as well as the model training time is reduced by 23%, and the types of parameters required to train the model are reduced by 50%. It accomplished this by effectively reducing data dimensions, discarding non-essential variables for NOx prediction, and diminishing computational complexity. Six input parameters were identified as pivotal in reconstructing the modeling data for accurate NOx prediction: engine speed, torque, EGR valve position, exhaust flow rate, air mass flow rate, and oil temperature. In optimizing the NOx prediction model, it is crucial to determine an optimal number of input parameters. Excessive parameters do not enhance model accuracy, while an insufficient number impairs the model’s ability to precisely emulate the NOx generation process, leading to diminished predictive accuracy.

Accurate Oil Temperature Prediction Model and Oil Refilling Parameters Optimization for Hydraulic Closed-Circuit System

Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 °C and 1.05 °C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems.

Experimental Performance Evaluation of a Lightweight Additively Manufactured Hydrodynamic Thrust Bearing

In this paper, a lightweight additively manufactured (AM) fixed geometry hydrodynamic thrust bearing fabricated via laser powder bed fusion (LPBF) is experimentally compared to a traditionally manufactured cast aluminum alloy thrust bearing of similar design. The purpose of this study is to evaluate how weight-saving design features in the AM bearing affect active critical hydrodynamic performance parameters to better understand in-service viability. Under various static operating conditions, performance parameters such as hydrodynamic pressure distribution, minimum oil film thickness (MOFT), bearing temperature and increase in oil temperature are measured. Compared to the traditionally manufactured bearing, the AM bearing showed an average increase in minimum oil film thickness of 53%, an average increase in trailing edge hydrodynamic pressure of 116%, while exhibiting an average decrease in bearing temperature of 1%. Experimental results are compared to numerical simulation showing reasonably good agreement.

Research on wax removal and prevention technology of White Wolf City oil well

Abstract Oil well wax removal technology is an important subject in the process of oil field exploitation. This paper analyzes the mechanism and characteristics of wax formation in oil Wells by measuring the amount of wax in crude oil in Baiwolcheng oil area. When the waxy component, which usually exists in liquid form, is accompanied by crude oil from underground to the ground, the waxy crystal precipitates and deposits in the inner wall of oil pipe due to the gradual decrease of the temperature of crude oil. The phenomenon of paraffin formation, paraffin formation process and paraffin formation law of oil well are studied, and chemical paraffin removal and anti-wax technology is determined as the main research object, evaluation and optimization of paraffin removal and anti-wax agent, and determination of dosage concentration and dosage.

Research and Application of Online Electromagnetic Heating System with Internal Penetration in Oil Pipeline at Well Site

Abstract In response to the current situation of heating crude oil in the oil pipeline of Changqing Oilfield, most of the well sites have no associated gas, gas heating furnaces have been discontinued, and coal-fired heating furnaces pollute the environment. A study was conducted on the online through type heating system for the oil pipeline in the well site, with a focus on the composition and function of the digital heating system for the through type oil pipeline in the well site. The through type online heating system for the oil pipeline in the well site is mainly composed of a variable frequency electromagnetic controller The online controller and internal heating cable are composed of three parts. The oil pipeline generates heat by itself, while preventing the crystallization of paraffin and scale in the oil pipeline under the action of alternating electromagnetic fields, which is not easy to form wax. In 2023, on-site application was carried out on two oil pipelines in the Tongzhai operation area of the Third Oil Production Plant in Changqing Oilfield, replacing the existing gas heating furnaces and coal-fired heating furnaces to heat and transport crude oil in the oil pipelines. It does not generate heat itself and has a long service life. Due to the high thermal efficiency of the heat source coming from the inside of the oil pipeline, the buried pipeline renovation can be installed without breaking the soil. The flexible internal cable reduces the difficulty of construction and is safe and environmentally friendly, At the same time, it can remotely monitor the temperature of crude oil in the oil pipeline and the wellhead backpressure online.