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

Mazahir Hussain, Shuang Liu, Umar Ashraf, Muhammad Ali, Wakeel Hussain, Nafees Ali and Aqsa Anees
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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
Aqsa Anees: Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China

Energies, 2022, vol. 15, issue 12, 1-15

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.

Keywords: self-organizing map; cluster analysis; lithofacies; Zamzama gas field; rock type (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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