Novi Labs

Novi Releases Data Engine!

After six years of building machine learning models for unconventional development, we have learned a HUGE amount about what it takes to build analytics-ready datasets. It’s not easy: messy tables, multiple sources, industry-specific challenges, and complicated transformations to get the data right.

Now, we are putting all of our experience and (and custom data-processing algorithms) in your hands with Data Engine. With our V1 release, we are starting at the beginning of the process: uploading, mapping, and formatting the data sources.

Data Engine V1 provides immediate benefit by shortening time-to-insights and putting more control of the data in YOUR hands. But beyond that, it represents the first step towards Novi as a full self-service platform.

Interested in learning more? Fill in the form below and we'll be in touch.
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More resources

Reviewing Delaware Basin production drivers with Novi Cloud Outputs

Novi Releases Data Engine!

Novi Presents at SPE Dallas Section Study Group: Machine Learning 101 in the Midland Basin

AAPG 2020: Before the Basin Model: Using ML & Formation Tops to Understand the Effect of Burial History (ID 4694)

AAPG 2020: Tailoring Completions to Geology: A Machine Learning Approach (ID 4686)

AAPG 2020: Prior Well Depletion or Interwell Spacing? Isolating the Causes of Spacing Degradation in the Williston Basin (ID 4697)

Novi Water Modeling featured in JPT September Issue

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)

URTeC 2020: Predicting Water Production in the Williston Basin using a Machine Learning Model (ID 2756)

URTeC 2020: Decomposition of Publicly Reported Combined Hydrocarbon Streams using Machine Learning in the Montney and Duvernay (ID 2795)

URTeC 2020: GeoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis (ID 2743)

URTeC 2020: The Impact of Interwell Spacing Over Time: A Machine Learning Approach (ID 2676)

URTeC 2020: The Impact of Spacing and Time on Gas/Oil Ratio in the Permian Basin: A Multi-Target Machine Learning Approach (ID 2800)

URTeC 2020: Evaluating the Impact of Precision Targeting on Production in the Midland Basin using Machine Learning Algorithms (ID 3062)

URTeC 2020: Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model (ID 2750)

Contributing Wells — Using Novi Production Modeler Software to See Which Wells Contributed to the Prediction

Using Prediction Engine to Evaluate Whiting’s Undrilled Acreage

Impact of delay-and-infill strategy on recoveries and returns :: Parsley – Jagged Peak acreage

Using game theory and confidence interval outputs from a machine learning model to risk adjust oil well returns

Iterating and optimizing completion designs across NBL’s entire Delaware DUC inventory in minutes

Given sub-$30 wti strip, how can NBL maximize returns on their DUC inventory?

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Deriving a regional subsurface model in a day from logs and tops – Novi Subsurface data extraction workflow

AAPG Explorer article “Is the Next Oil Production Breakthrough Already Here?” featuring Novi President Jon Ludwig

Prediction Engine Version 2 Software Demonstration

SHELL iSHALE® COLLABORATES WITH NOVI

JPT Article Featuring Novi – Rapid Evaluation of Development Ideas Has Engineers Thinking: What If?

Novi / Range Resources SPE-191796-18ERM-MS: Integrating Big Data Analytics Into Development Planning Optimization

Co-Develop vs. Infill – A Complex Question

Barclays Bank "Frac to the Future" Analyst Report (rel: January 2020)

Evaluating PV10 acquisition scenarios using Novi Cloud outputs in Spotfire

Using Novi Prediction Engine to evaluate Parsley Energy’s acquisition of Jagged Peak

Novi AAPG ACE 2019 Paper – Production and Subsurface Machine Learning Model for Predicting Hydrocarbon Recovery

Picking the Best Statistical Approach for Modeling Hydrocarbon Systems

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