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

doi:10.36435/jgf.v18i1.418

Hadyan Pratama Department of Geophysical Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Loris Alif Syahputra Department of Geophysical Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Muhammad Fauzan Albany Department of Geophysical Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Agus Abdullah Department of Geophysical Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Sandy Kurniawan Suhardja Department of Geophysical Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Epo Kusumah Department of Geological Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Weny Astuti Department of Petroleum Engineering University of Pertamina, Jl. Teuku Nyak Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia
Bambang Mujihardi Upstream Researh and Technology - RTC, PT Pertamina (Persero), Jakarta 10110, Indonesia

DOI: http://dx.doi.org/10.36435/jgf.v18i1.418

Keywords

Artificial Intelligence, K-Nearest Neighbors, Artificial Neural Network, Facies Estimation

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.

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