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

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