![[URTeC 2020] Deriving Time-Dependent Scaling Factors for Completions Parameters in the Williston Basin using a Multi-Target Machine Learning Model and Shap Values (ID 3103)](https://novilabs.com/wp-content/uploads/2020/07/image-18-1024x657-1.png)
Applying a single scaling factor over the life of the well for a completions variable will give you an inaccurate production profile ...
![[URTeC 2020] Predicting Water Production in the Williston Basin using a Machine Learning Model (ID 2756)](https://novilabs.com/wp-content/uploads/2020/07/image-17-1024x900-1.png)
[URTeC 2020] Predicting Water Production in the Williston Basin using a Machine Learning Model (ID 2756)
Like it or not, most companies don't spend nearly as much time analyzing water production as oil (and who can blame them!?!). ...
![[URTeC 2020] Decomposition of Publicly Reported Combined Hydrocarbon Streams using Machine Learning in the Montney and Duvernay (ID 2795)](https://novilabs.com/wp-content/uploads/2020/07/image-16-1024x770-1.png)
[URTeC 2020] Decomposition of Publicly Reported Combined Hydrocarbon Streams using Machine Learning in the Montney and Duvernay (ID 2795)
Canadian operators have an information problem: in the public data, "gas" wells don't have to report their liquids content. This means that ...
![[URTeC 2020] GeoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis (ID 2743)](https://novilabs.com/wp-content/uploads/2020/07/image-15-1024x716-1.png)
[URTeC 2020] GeoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis (ID 2743)
Since Novi was founded, customers, partners, and prospects have been asking us if machine learning could be used to extract a subsurface ...
![[URTeC 2020]The Impact of Interwell Spacing Over Time: A Machine Learning Approach ( id 2800)](https://novilabs.com/wp-content/uploads/2020/07/image-21-1024x376-1.png)
[URTeC 2020]The Impact of Interwell Spacing Over Time: A Machine Learning Approach ( id 2800)
How many times have the early results for a tight spacing test "looked great," with production dropping precipitously thereafter? Peak rates, 90-day ...
![[URTeC 2020] The Impact of Spacing and Time on Gas/Oil Ratio in the Permian Basin: A Multi-Target Machine Learning Approach (ID 2676)](https://novilabs.com/wp-content/uploads/2020/07/image-22-1024x689-1.png)
[URTeC 2020] The Impact of Spacing and Time on Gas/Oil Ratio in the Permian Basin: A Multi-Target Machine Learning Approach (ID 2676)
As unconventional, horizontal development in the Permian Basin moves beyond adolescence into middle age, operators will increasingly have to deal with older ...
![[URTeC 2020] Evaluating the Impact of Precision Targeting on Production in the Midland Basin using Machine Learning Algorithms (ID 3062)](https://novilabs.com/wp-content/uploads/2020/07/image-20-1024x674-1.png)
[URTeC 2020] Evaluating the Impact of Precision Targeting on Production in the Midland Basin using Machine Learning Algorithms (ID 3062)
With the right dataset and right machine learning model, you can get high-resolution answers on fine-tuning your target selection -- potentially increasing ...
![[URTeC 2020] Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model (ID 2750)](https://novilabs.com/wp-content/uploads/2020/07/image-19-1024x858-1.png)
[URTeC 2020] Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model (ID 2750)
How can you properly benchmark operators -- to identify overperformers to learn from, or underperformers to acquire? Our machine learning models provide ...
