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
Enter your name and email below. We will email the presentation to you right away.
Latest Resources
[Data Engine] Merging multiple source files and building a dataset
Looking for a faster and more efficient way to build analytics-ready datasets? Join Charles Kosa, Novi’s Head of Customer Success, as he takes you on …
[URTeC 2022] The Diminishing Returns of Lateral Length Across Different Basins (ID 3723784)
The Diminishing Returns of Lateral Length Across Different Basins Talk Details:: – Tuesday, June 21st at 2:15 PM | Room 371 – Theme 7: Applications …
[URTeC 2022] Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning
Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning AUTHORS DP. Zannitto2, C. Kosa1 (1. Novi Labs 2. Shell Compania …
News, trends and data for the US upstream industry
Novi Energy Newsletter
