Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches

Creative Commons License

Temizel C., Odi U., Balaji K., Aydin H., Santos J. E.

ENERGIES, cilt.15, sa.20, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 20
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/en15207660
  • Dergi Adı: ENERGIES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: supervised learning, unsupervised learning, classification, CLASSIFICATION, LITHOLOGY
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Lithology is one of the critical parameters influencing drilling operations and reservoir production behavior. Well completion is another important area where facies type has a crucial influence on fracture propagation. Geological formations are highly heterogeneous systems that require extensive evaluation with sophisticated approaches. Classification of facies is a critical approach to characterizing different depositional systems. Image classification is implemented as a quick and easy method to detect different facies groups. Artificial intelligence (AI) algorithms are efficiently used to categorize geological formations in a large dataset. This study involves the classification of different facies with various supervised and unsupervised learning algorithms. The dataset for training and testing was retrieved from a digital rock database published in the data brief. The study showed that supervised algorithms provided more accurate results than unsupervised algorithms. In this study, the extreme gradient boosted tree regressor was found to be the best algorithm for facies classification for the synthetic digital rocks.