Open AccessPreliminary sandstone reservoir depth prediction with pre-processing data using principle component analysis (PCA) and partial least square (PLS) based on well logging data attribute
Author(s) -
Erna Utami,
Mauridhi Hery Purnomo,
Rezha F. Rizki,
T. R. Biyanto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1517/1/012086
Subject(s) - multicollinearity , statistics , principal component analysis , artificial neural network , standard deviation , partial least squares regression , multilayer perceptron , well logging , regression analysis , mathematics , pattern recognition (psychology) , data mining , geology , artificial intelligence , computer science , petroleum engineering
Sandstones containing 60% of oil or gas because of porosity and permeability. The conventional method required sandstones predicted with complete data. This study used neural networks to determine the depth of sandstones and predict incomplete variables well site at the Sunda Strait-south area. Data pre-processing used PCA-PLS to find the most important variables that affect the output thus improving the prediction results. Multicollinearity analysis is used to determine the data compression needed. Raw data multicollinearity result showed that the multicollinearity occurs indicated with VIF over 10 and tolerance under 0.1 at CALI and SP variables. The PCA-PLS analysis uses to reduce data from 13 variables into six important variables namely DEPT GR RHOB NPHI ILD Peff, these results do not experience multicollinearity. The variable that predicted is ILD with the best ANN multilayer perceptron showed small standard deviation and standard error results 2.85 and 0.03. The best ANN model to predict the depth of radial basis sandstone is due to produce a regression test of 0.8 based on the results of the validation of the log image.