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All Journal Jurnal Geofisika
Weny Astuti
Department of Petroleum Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Earth & Planetary Sciences Energy Physics

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Early Results of Comparison between K-Nearest Neighbor and Artificial Neural Network Method for Facies Estimation Hadyan Pratama; Loris Alif Syahputra; Muhammad Fauzan Albany; Agus Abdullah; Sandy Kurniawan Suhardja; Epo Kusumah; Weny Astuti; Bambang Mujihardi
Jurnal Geofisika Vol 18 No 1 (2020): Jurnal Geofisika
Publisher : Himpunan Ahli Geofisika Indonesia (HAGI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36435/jgf.v18i1.418

Abstract

Artificial Intelligence method has been widely used recently in many aspects to understand big data. Fundamentally, the purpose of Artificial Intelligence is to solve nonlinear problem. Most methods are trying to optimize an output from one or many inputs parameter by identifying any potential patterns that fit or using a statistical data. In Oil & Gas industry, one of the main challenges that can be solved by Artificial Intelligence is estimating facies from well log or seismic data. The main scope of this study is estimating lithofacies by analyzing well logs input using two different methods, K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). We employed various well log data such as gamma-ray, resistivity, neutron density porosity, and photoelectric effect from well log data at Panoma Council Grove Field, South West Kansas, United States. This study shows that using optimized parameters, KNN method faster than ANN method but, ANN give result better than KNN. Nevertheless, despite the fact this research could estimate lithologies, many aspect should be considered in order to reach optimum result such as insights from geological regional models.
Co-Authors Agus Abdullah Bambang Mujihardi Epo Kusumah Hadyan Pratama Loris Alif Syahputra Muhammad Fauzan Albany Sandy Kurniawan Suhardja