Data Quality Considerations for Petrophysical Machine Learning Models

It is essential that good quality and appropriately selected data is used when building petrophysical machine learning models, otherwise the predictive capability of the selected algorithm could be impacted. In this week’s video I present a similar presentation to the one I gave at the SPWLA 2021 Annual Conference, which covers key data quality issues faced when working with well log data. If you want, you can check the original paper, and the related journal paper at the links below: Conference Paper: The journal Paper, which contains more information on validating ML models: If you haven’t already, make sure you subscribe to the channel: ----- RECOMMENDED BOOKS As an Amazon Associate I earn from qualifying purchases. By buying through any of the links below I will earn commission at no extra cost to you. PYTHON FOR DATA ANALYSIS: Data Wrangling with Pandas, NumPy, and IPython UK: h