[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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Accurate forecast on parent-child developments

In this live webinar, you will learn how Novi’s new algorithm improves model sensitivity for spacing and parent-child scenarios, providing powerful results for previously difficult-to-analyze problems.

Ted Cross, our VP of Product Management, will show you how this update improves spacing and infill scenario analysis without sacrificing model accuracy.