Log data recorded by wireline tools are incomplete in most well locations. Vital information often needs to be predicted to precisely characterise the Earth's subsurface. Here we describe a machine learning (ML) workflow to predict missing data in well logs at the basin scale. The ML models produce outstanding results when adequate quality data is provided for the model training and inference. Using examples from the Permian Basin in the US, we illustrate the use of the automated data clean-up pipeline and the clean-up impact on ML algorithm training and prediction. The ML models achieve a prediction quality of 90% to 95% in a blind test containing 679 wells if trained on clean data from the Permian Basin.
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