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A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network

Author

Listed:
  • Chen, Hao
  • Wang, Yu
  • Zuo, Mingsheng
  • Zhang, Chao
  • Jia, Ninghong
  • Liu, Xiliang
  • Yang, Shenglai

Abstract

Diffusion is the key mechanism of enhanced oil recovery (EOR) by CO2 injection in unconventional oil reservoirs. The accurate measurement of the diffusion coefficient in porous media is essential for forecasting and optimizing CO2 injection. The pressure decay technique is the most commonly used method for measuring the diffusion coefficient, which is well acknowledged. However, it has a long experimental period with higher requirements on the equipment and operation. This paper firstly proposed a quick and simple prediction methods of diffusion coefficient for both CO2-oil systems within/without porous media based on back propagation (BP) neural network. The average errors are 18.73% and 18.80%, respectively. With the continuous supplement of the data, models can be continuously updated to provide more accurate estimates of the supercritical CO2-oil system without/with porous media conditions. Temperature, pressure, permeability, porosity and surface area positively correlate with the diffusion coefficient. Oil viscosity, oil density, and volume of porous media have a negative correlation with the diffusion coefficient. It is worth noting that for rocks with certain volume, the increase of surface area can significantly increase the diffusion coefficient, which implies that direct upscale of the measured CO2 diffusion coefficient in the lab is totally unreasonable.

Suggested Citation

  • Chen, Hao & Wang, Yu & Zuo, Mingsheng & Zhang, Chao & Jia, Ninghong & Liu, Xiliang & Yang, Shenglai, 2022. "A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221025342
    DOI: 10.1016/j.energy.2021.122286
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    References listed on IDEAS

    as
    1. Qian, Jiaxin & Wu, Jiahui & Yao, Lei & Mahmut, Saniye & Zhang, Qiang, 2021. "Comprehensive performance evaluation of Wind-Solar-CCHP system based on emergy analysis and multi-objective decision method," Energy, Elsevier, vol. 230(C).
    2. Chao Zhang & Chenyu Qiao & Songyan Li & Zhaomin Li, 2018. "The Effect of Oil Properties on the Supercritical CO 2 Diffusion Coefficient under Tight Reservoir Conditions," Energies, MDPI, vol. 11(6), pages 1-20, June.
    3. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    4. Jia, Xiaoliang & An, Haizhong & Fang, Wei & Sun, Xiaoqi & Huang, Xuan, 2015. "How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective," Energy Economics, Elsevier, vol. 49(C), pages 588-598.
    5. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    6. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
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    Cited by:

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    3. Lv, Qichao & Rashidi-Khaniabadi, Ali & Zheng, Rong & Zhou, Tongke & Mohammadi, Mohammad-Reza & Hemmati-Sarapardeh, Abdolhossein, 2023. "Modelling CO2 diffusion coefficient in heavy crude oils and bitumen using extreme gradient boosting and Gaussian process regression," Energy, Elsevier, vol. 275(C).
    4. Wang, Zhoujie & Zhu, Jianzhong & Li, Songyan, 2023. "Novel strategy for reducing the minimum miscible pressure in a CO2–oil system using nonionic surfactant: Insights from molecular dynamics simulations," Applied Energy, Elsevier, vol. 352(C).
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    6. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
    7. Li, Bo & Yu, Hao & Xu, WenLong & Huang, HanWei & Huang, MengCheng & Meng, SiWei & Liu, He & Wu, HengAn, 2023. "A multi-physics coupled multi-scale transport model for CO2 sequestration and enhanced recovery in shale formation with fractal fracture networks," Energy, Elsevier, vol. 284(C).

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