Novi Labs

The Power of Novi 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.

We have put all of our experience and (and custom data-processing algorithms) in your hands with Data Engine. Anyone working in Data Science of Machine Learning will attest that building the input dataset is the most time-consuming, error-prone part of the dataset. Data Engine makes it quick, easy, painless, and repeatable to build analytics-ready datasets.

Interested in learning more? Fill in the form below and we'll be in touch.
  • This field is for validation purposes and should be left unchanged.

More resources

Generating PDP Oil & Gas Forecasts with Novi Machine Learning Technology

The Power of Novi Data Engine

Reviewing Delaware Basin production drivers with Novi Cloud Outputs

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?

Changing plans on a dime: analyzing QEP’s Midland asset given sub-$30 WTI strip with Novi Prediction Engine

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

5 Ways to Prevent Over-Engineered and Overpriced Wells