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Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

Author

Listed:
  • Mazahir Hussain

    (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Shuang Liu

    (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Umar Ashraf

    (Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China)

  • Muhammad Ali

    (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Wakeel Hussain

    (Department of Geological Resources and Engineering, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Nafees Ali

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Aqsa Anees

    (Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China)

Abstract

Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.

Suggested Citation

  • Mazahir Hussain & Shuang Liu & Umar Ashraf & Muhammad Ali & Wakeel Hussain & Nafees Ali & Aqsa Anees, 2022. "Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type," Energies, MDPI, vol. 15(12), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4501-:d:843503
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    References listed on IDEAS

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    1. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
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    Cited by:

    1. Nafees Ali & Xiaodong Fu & Umar Ashraf & Jian Chen & Hung Vo Thanh & Aqsa Anees & Muhammad Shahid Riaz & Misbah Fida & Muhammad Afaq Hussain & Sadam Hussain & Wakeel Hussain & Awais Ahmed, 2022. "Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    2. Ren Jiang & Zhifeng Ji & Wuling Mo & Suhua Wang & Mingjun Zhang & Wei Yin & Zhen Wang & Yaping Lin & Xueke Wang & Umar Ashraf, 2022. "A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir," Energies, MDPI, vol. 15(19), pages 1-20, September.
    3. Wakeel Hussain & Muhsan Ehsan & Lin Pan & Xiao Wang & Muhammad Ali & Shahab Ud Din & Hadi Hussain & Ali Jawad & Shuyang Chen & Honggang Liang & Lixia Liang, 2023. "Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China," Energies, MDPI, vol. 16(6), pages 1-25, March.

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