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)
Applying a single scaling factor over the life of the well for a completions variable will give you an inaccurate production profile — and inaccurate economics! Quantifying the time-variant impact of completions decisions is tough for traditional methods. However, training a machine learning model to predict a time series of production, combined with Shapley values, provides a powerful tool to investigate when completions are most impactful.
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!?!). Fortunately, machine learning techniques developed for forecasting oil production can be adapted for water production — it’s part of our standard model delivery.
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. Novi has developed a solution to estimate CGR that’s twice as accurate as the traditional methods.
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 model directly from logs. The answer is yes.
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 production by over 10% at zero cost.
Using Prediction Engine to Evaluate Whiting’s Undrilled Acreage
What are the best remaining acreage blocks in Whiting’s North Dakota portfolio? With Prediction Engine, you can quickly and easily lay out well sticks and get predictions for different designs. Here, we walk you through how to configure & generate economics across the acreage.
Impact of delay-and-infill strategy on recoveries and returns :: Parsley – Jagged Peak acreage
At $30 oil, marginal zones become uneconomic. What is the impact on EURs and returns if those zones are infilled two years in the future? In this video, we take a look at the impact of delays on Bone Springs Third Sand and Wolfcamp A, B, and C zones in Parsley’s newly-acquired legacy Jagged Peak acreage in the southeastern Delaware Basin.
Using game theory and confidence interval outputs from a machine learning model to risk adjust oil well returns
In this video we walk through how Novi Shapley data and confidence interval data can be used to gain confidence in machine learning model predictions and handicap possible well performance using model confidence as a proxy for risk. You can review the blog post here.
Iterating and optimizing completion designs across NBL’s entire Delaware DUC inventory in minutes
In this Novi product video, Novi Technical Advisor John Chaplin walks through how a user can create Novi Prediction Engine Outputs, using NBL’s DUC inventory as an example, and publish these to Novi Cloud for analysis. To see the full description of the inputs and how we put this together read the blog post here.
Given sub-$30 wti strip, how can NBL maximize returns on their DUC inventory?
In this Novi product video, Novi Technical Advisor John Chaplin walks through an analysis of NBL’s Delaware DUC inventory and suggests changes in completion design in this sub $30 WTI price environment based on Novi Prediction Engine Outputs published to Novi Cloud. To see the full description of the inputs and how we put this together read the blog post here.
Changing plans on a dime: analyzing QEP’s Midland asset given sub-$30 WTI strip with Novi Prediction Engine
In this Novi product video, Novi Technical Advisor John Chaplin walks through and analysis of QEP’s Midland asset and suggests changes in capital allocation in this sub $30 WTI price environment based on Novi Prediction Engine Outputs published to Novi Cloud. To see the full description of the inputs and how we put this together read the blog post here.
Latest news & updates
Digital Wildcatters Interview of Novi President Jon Ludwig
Novi President Jon Ludwig sits down with the cool cats at Digital Wildcatters for their first ever “video included” DW podcast and discusses Novi’s roots and the future of decision making in shale as continued pressure on strip price forces oil companies and investors to re-evaluate their decision making paradigms.
Open Position: Senior QA Automation Engineer
Join our talented engineering team to lead our quality strategy and develop our automation platform. This role includes both autonomy and responsibility. Become a leader on our technology team and help Novi Labs build a world-class machine learning platform for the energy industry.
Join Us at SPE HFTC 2020
The SPE Hydraulic Fracturing Technology Conference showcases existing and new hydraulic fracturing technologies, using experiences from fracture-stimulated wells, and the application of global learnings.