Local mean imputation for handling missing value to provide more accurate facies classification

Abstract

The search for oil and gas reserves is an ongoing challenge. The possibility of oil and natural gas reserves running out in Indonesia in less than 25 years must also be considered by the industry. Unfortunately, the process of exploration and development of hydrocarbon fields still has its challenges, one of which is knowing the characteristics of hydrocarbon reservoirs. One of these characteristics that should be analyzed is rock facies. This could be done by using core data, well logs, analog studies, and others. However, this method is very expensive and time-consuming. Currently, the machine learning approach is the most popular solution for the classification of rock facies. However, a common issue with this method is the missing values due to recording errors in the field. Meanwhile, a machine learning system requires accurate data to form a good model. Therefore, we propose the use of a simple imputation method to solve the local mean imputation method by considering the uniqueness of each class before performing the imputation process. We also implemented several popular boosting methods in machine learning such as comparing Extreme Gradient Boosting and Light Gradient Boosting with several machine learning algorithms that are often used in solving facies classification problems such as Random Forest, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. According to our results, the Local Mean imputation method improved the model's performance, where the perceived increase was between 1-10% and the model with the best performance was generated by the Random Forest method.