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Inversion of well logs into rock types, lithofacies and environmental facies, using pattern recognition, a case study of carbonate Sarvak Formation

  • Moradi, Majid1
  • Tokhmechi, Behzad1
  • Masoudi, Pedram2, 3
  • 1 Shahrood University of Technology, School of Mining, Petroleum and Geophysics Engineering, Shahrood, Iran , Shahrood (Iran)
  • 2 University of Tehran, School of Mining Engineering, College of Engineering, North Kargar, Tehran, 1431954378, Iran , Tehran (Iran)
  • 3 CNRS UMR6118, Univ. de Rennes 1, Géosciences-Rennes, Bat.15, Campus de Beaulieu, Rennes Cedex, 35042, France , Rennes Cedex (France)
Published Article
Carbonates and Evaporites
Springer Berlin Heidelberg
Publication Date
Oct 06, 2017
DOI: 10.1007/s13146-017-0388-8
Springer Nature


AbstractThe “facies” is a frequently used term for describing sedimentary units. In the literature, this term has been used for different purposes, as depositional environment, rock type, lithofacies, etc. In subsurface geology, the core samples are essential for facies studies. While lacking cored intervals, the well logs provide precious subsurface information, but the complexity of well log responses leads most of the time to the complexity of interpretations. In this paper, a method is proposed to study the facies types through well logs. The case study is a carbonate platform system, deposited in the upper Cretaceous, named Sarvak Formation, in one of Iranian onshore oilfields, located in the Abadan Plain. For this purpose, parametric and non-parametric (k-nearest neighbor) classifiers were applied to the dataset. Detailed petrography, assisted by core descriptions, led to 37 microfacies, grouped into three main lithofacies, four carbonate rock types and six environmental facies. Classifiers could not identify the microfacies due to a limited number of observations and high variations. However, the environmental facies were truly classified. In addition, lithofacies classification and Dunham carbonate rock typing were carried out correctly. It is discussed that k-nearest neighbor is clearly the outperformed classifier, and the classical parametric models were inadequate due to the nature of the input well logs (dependency of well logs and may be their distribution), and the overlapping of the input feature space.Graphical abstract

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