Imputation of missing values in well log data using k-nearest neighbor collaborative filtering

Abstract

Well log data provide key subsurface information, which is crucial for lithology evaluation and reservoir characterization. However, due to technical issues, well log data may contain missing values at certain depth intervals, which can be detrimental for data analysis. The best method is to reacquire the missing data by relogging, but this increases operational costs. Thus, a cost-efficient method for restoring the lost data is needed to overcome this issue. We propose an imputation method for missing well log data using collaborative filtering, a widely used algorithm for making new item recommendations to users. Although collaborative filtering is mainly used in recommendation systems, its fundamental principle allows us to utilize it to help make predictions for missing log data. The method is applied to a well log dataset obtained from the North Sea near Norway. The results show that the collaborative filtering algorithm has the potential to be a powerful imputation method for missing well log data, but there are some limitations that need to be addressed.