[AAPG 2020] Before the Basin Model: Using ML & Formation Tops to Understand the Effect of Burial History

What is the minimum amount of geological data required to build a predictive model?

Traditional basin modeling approaches utilize expensive datasets and significant time expenditure to understand and predict fluid distribution around the basin. Machine learning offers an empirical method to generate similar predictions. In contrast to the forward-modeling method of traditional basin modeling, machine learning looks for implicit relationships between training and target variables.

In this study, we investigate whether a machine learning model fed only geologic tops as input geological data can accurately predict well performance in the Bakken-Three Forks play of the Williston Basin

POSTER Details::

Tuesday, Wednesday, and Thursday, 9-10 AM & 3-4 PM (central time). Theme 3: Geochemistry, Basin Modeling, and Petroleum Systems

Depth of the Triassic Spearfish Formation (one of the training tops), and its SHAP values. The deep Spearfish in the southwestern part of the basin drives down the model forecast in the area.

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