The Application of Parametric and Nonparametric Regression to Predict The Missing Well Log Data

Mordekhai Mordekhai, Izzul Qudsi, John Papilon Steven Guntoro

Abstract


Incomplete well log data are very commonly encountered problems in petroleum exploration activity. The development of artificial intelligence technology offers a new possible way to predict the required logs using limited information available. Optimizing conventional statistical theory, machine learning is proven to be a reliable tool for any prediction task in many fields of study. Regression is one of the basic methods that has rapid development and evolved many techniques with different approaches and purposes. In this study, parametric and nonparametric regressions {linear regression, Support Vector Machine (SVM), and Gaussian Process Regression (GPR)} are compared to predict the missing log using the available nearby data. Feature selection was done by performing Principal Component Analysis (PCA) on predictor variables. Different profile of PCA is observed between Cibulakan and Parigi Formations, which is the basis of conducting separate models based on the formation. Among all the selected methods, GPR is consistently making slightly better results. The correlation between the predicted and actual porosity of GPR is observed to be up to 0.19 higher compared to the other methods. Similar observation is also found on the Root Mean Squared Error (RMSE) value comparison. In practice, the GPR method has an inherent advantage compared to other methods, as it provides uncertainty to the prediction based on the standard deviation of each estimation result. The standard deviation of the GPR prediction ranges from 0.006 in high confidence cases and up to 0.077 where uncertainty is high. The models are considered robust and stable according to the RMSE evaluation from cross validation which is consistently giving the value below 0.04. In conclusion, the reliability of regression techniques for predicting the missing well log is exposed in this study, which results demonstrate steady and good accuracy in every formation which are tested on any well logs.



Keywords


Well Log; Log Prediction; Regression; Artificial Intelligence; Machine Learning

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