Using AI tools to fill an incomplete well log dataset: A workflow

Abstract

One issue commonly found when working with well log data is the irregular abundance/availability of the different recorded parameters. This is especially applicable when working with datasets collected in different campaigns that may span through the years, even decades, or different companies. Artificial Intelligence may be useful to fill gaps in the original database, resulting in a more complete, standardised one. In this work we present a workflow that can be followed to fills gaps in a dataset using different AI techniques. It consists of four main steps: 1) feature combination selection; 2) hyperparameter tuning; 3) performance assessment and best option choice; 4) blind testing. The process can be performed iteratively, successively populating the database with missing parameters, starting with those for which there are more available training data and whose results are more reliable. In this work, we present an example in which we filled an incomplete dataset consisting of wells provided by the UK National Data Repository (NDR) of the Oil & Gas Authority (OGA). The performance of some of the most commonly used artificial intelligence methods (support vector machine, random forest, multi-layer perceptron) was tested varying their hyperparameters until reaching an adequate result.