Effect of laser irradiation on a heavy crude oil sample: Changes in viscosity and implications for oil recovery and transport

Zhang, Shanzhe; Sun, Xiaowen; Yan, Sining; Liu, Cuiling; Miao, Xinyang; Zhao, Kun

doi:10.1063/5.0130925

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Laser technology has also been used for the monitoring of laser drilling, identification of crude oils, characterization of water content in crude oil emulsion, measurement of wax appearance temperature, characterization of viscosity change for crude oils. [29][30][31][32][33] Moreover, THz method has been widely used in the oil field. THz-TDS has been used to characterize oil disaggregation under magnetic field and analyze the state of oil-water two-phase flow.…”

Section: Resultsmentioning

confidence: 99%

Terahertz spectroscopy combined with machine-learning models for crude oil classification

Zhang,

Zheng,

Sun

et al. 2023

TST

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The classification of crude oils plays an important role in the petroleum transportation and production. In this paper, terahertz time-domain spectroscopy (THz-TDS) is used to assess seven various crude oils combined with machine-learning algorithms. From THz-TDS, frequency, refractive index and absorption coefficient are used to set models, which are based on Extreme Gradient Boosting (XGBoost), Random Forest (RF) and K-Nearest Neighbors (KNN), respectively. In order to evaluate the accuracy of each model, the confusion matrix and the Area under the curve (AUC) are introduced to access the classification ability, and 5-fold cross-validation are used to compare the generalization ability and robustness. Compared to other models, the classification accuracy of XGBoost reaches the maximum 0.9622. Meanwhile, the test 5-fold cross-validation F1-score and the AUC of XGBoost model are higher than other models, which indicates the high consistency and robustness. Experimental results suggests that terahertz time-domain spectroscopy may be a powerful tool for the identification of various crude oils.

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Section: Resultsmentioning

confidence: 99%

Terahertz spectroscopy combined with machine-learning models for crude oil classification

Zhang,

Zheng,

Sun

et al. 2023

TST

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Robust pressure prediction of oil and gas pipeline networks based on equipment embedding neural network

Jiang,

Li,

Yuan

et al. 2024

Physics of Fluids

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Currently, extensive pipeline networks are developed in response to the demands of the oil and gas industry. The accurate estimation of the hydraulic condition of pipeline networks holds significant importance in the fields of pipeline design and safety management. Nevertheless, predicting the pressure of oil and gas pipeline networks with different equipment and structures remains challenging. To meet this challenge, a novel pressure prediction model for the oil and gas pipeline networks based on the equipment embedding neural network (EENN) is proposed in this study. The proposed model embeds different equipment models into the neural network model. The neural network in this model is used to focus on learning the connection characteristics of the pipeline network to achieve higher prediction accuracy. The present study first explores different embedding combinations of the EENN model to estimate the pressure in an oil pipeline network system that involves a non-isothermal batch transportation process. Then, the trained model is applied to predict the pressure in a gas pipeline network. The optimal EENN exhibits an average prediction error of 18.5% for oil pipelines and 0.36% for gas pipelines, which is lower than 20.8% and 3.57% under the neural network. The findings of this study demonstrate the efficacy of the proposed EENN in accurately forecasting pressures in diverse oil and gas pipeline networks by reducing the complexity of the learning process.

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An energy perspective on the mechanism of crude oil electrorheological effect

Zhang,

Li,

Wang

et al. 2024

Physics of Fluids

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Exposing a waxy crude oil to an electric field represents an emerging environmentally sound strategy for improving the cold flowability of oil. However, a substantial knowledge gap still exists regarding the quantitative relationship between the viscosity reduction and treatment parameters (field strength, treatment time, the volume of treated oil, etc.). This study endeavors to investigate the physical essence of the effect of these treatment parameters on the viscosity reduction and its duration. It was found when subjected to electric fields of varying strengths (0.5–5 kV/mm) for sufficient time, a same maximum viscosity reduction of approximately 40% can be achieved regardless of the applied field strength. Further research has elucidated that the factor determining the viscosity reduction is energy input, rather than the field strength as was reported previously, and the inputted energy may work in three stages: first, it works for initiating a decrease in viscosity. Subsequently, the continued energy input further reduces the oil viscosity and ultimately achieves a maximum reduction at that temperature. Then further inputted energy enhances the duration of the viscosity reduction. Fundamentally, the inputted energy density, i.e., the inputted energy per unit volume/mass of the oil, is the essential factor. These new findings facilitate further understanding of the negative electrorheological effect and its mechanism of crude oil and may help for the development of electric treaters for reducing crude oil viscosity.

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Effect of laser irradiation on a heavy crude oil sample: Changes in viscosity and implications for oil recovery and transport

Cited by 5 publications

References 43 publications

Terahertz spectroscopy combined with machine-learning models for crude oil classification

Terahertz spectroscopy combined with machine-learning models for crude oil classification

Robust pressure prediction of oil and gas pipeline networks based on equipment embedding neural network

An energy perspective on the mechanism of crude oil electrorheological effect

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