Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type
نویسندگان
چکیده
Nowadays, there are significant issues in the classification of lithofacies and identification rock types particular. Zamzama gas field demonstrates complex nature due to heterogeneous reservoir formation, while it is quite challenging identify lithofacies. Using our machine learning approach cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects provide an accurate result even when user inexperienced. To constrain models, type a critical step characterization. Many empirical statistical methodologies have been established based on effect performance. Only well-logged data provided, no cores sampled. Given circumstances, fact that traditional methods such as regression intractable, chosen apply three strategies: (1) using self-organizing map (SOM) arrange depth intervals with similar facies into clusters; (2) clustering split various specific zones; (3) analysis technique used type. In field, SOM techniques discovered four group facies, each which was internally comparable petrophysical properties distinct from others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) Water Saturation (Sw) generate results. The findings behavior shows facies-01 facies-02 good characteristics for acting gas-bearing sediments, whereas facies-03 facies-04 non-reservoir sediments. outcomes this study stated excellent rock-type zone field.
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ژورنال
عنوان ژورنال: Energies
سال: 2022
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en15124501