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DUC, DUC, GOOSE: In a sub-$30 oil environment how can NBL maximize returns with less CAPEX spend in their Delaware asset?

3 days ago

change of plans: how QEP could optimize returns in their Midland asset in a low-price environment

1 week ago

five ways algorithms, compute and smart digital oilfield applications might save the Shale industry

1 week ago

less than 10% of remaining Bakken inventory is in the play core

2 weeks ago

spacing and completions: untangling complex interactions with machine learning

1 month ago

resources

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.

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

This video goes into detail on Novi’s subsurface model for the Williston Basin in North Dakota and is incorporated in a Novi blog post on this same subject. Novi algorithms analyzed & extracted log data, conducted a principal component analysis, and used that information for model training. When combined with SHAP values, this workflow becomes a powerful way to generate rock quality maps using machine learning.

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

Just prior to URTeC 2019, Barry Friedman of AAPG Explorer asked Novi President Jon Ludwig some important questions about the future of digital oilfield adoption – particularly when would rapid adoption start to happen across the industry.

Prediction Engine Version 2 Software Demonstration

A live video demonstration of Novi’s Prediction Engine version 2 software. Quickly create robust economic scenarios when valuing acreage, searching for the best development scenario given capital constraints, or understanding how Novi’s models interpret subsurface, stacking, spacing and stimulation intensity scenarios.

SHELL iSHALE® COLLABORATES WITH NOVI

Shell’s technology collaborations with Novi Labs LLC (“Novi”) promise to deliver smarter and more efficient wells in not too distant future.

“We are collaborating with our strategic partners to progress a technological step change in Shell’s shale assets and develop a shale field of the future.”

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

What started as a project to automate the tedious task of creating and updating well production forecasts at Range Resources led to a system that challenges engineers there to question the status quo.

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

This paper reviews several Big Data analytical initiatives in the Marcellus Shale. We describe how application of Big Data technology evolved, share challenges and benefits derived from Big Data analytical processes, and discuss lessons learned. We present an overview of Big Data methods employed, show how we integrated results with economic analyses to guide field development, and summarize the significant impact on development economics.

Co-Develop vs. Infill – A Complex Question

Should all formations be developed at the same time (co-develop) or, should an Operator develop first tier formation targets first, then infill secondary formations later? This video demonstrates how Novi Data consumed through Novi Cloud can help answer this complex question, and define the economic loss of infilling vs. co-development.

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

Read Barclays Bank report “Frac to the Future” for some great insight on the Digital Oilfield technology marketplace. In depth analysis of the “Big Three” (Microsoft, Google, AWS) cloud providers, smaller companies such as Novi, and the legacy software providers to the industry (HAL, SLB).

And, Novi has a nice profile in there, and a lot of input into the report straight to the Barclays guys.

Evaluating PV10 acquisition scenarios using Novi Cloud outputs in Spotfire

Novi Founder and President Jon Ludwig walks through how to use a business intelligence tool like Spotfire to connect to Novi Cloud and consume Novi Prediction Engine outputs. See how Novi’s data can be used to instantly calculate IRR, NPV, PVI, cash payback and cashflow from multiple possible development plans in a single pane.

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

In this video, Novi Technical Advisor Ted Cross will walk through a demonstration of Novi’s Prediction Engine software, just released on 02-February, 2020. See how Novi Prediction Engine will crush the amount of time it takes engineers, geoscientists, or financial analysts to do a very robust analysis of a play, both before its acquired, and after.

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

Download our AAPG ACE 2019 technical presentation!  Presented by Novi’s chief geophysicist Kiran Sathaye, the presentation focuses on building a predictive #OilAndGas recovery model based on public data only, including an automated framework to derive a subsurface model directly from electronic well logs.

Picking the Best Statistical Approach for Modeling Hydrocarbon Systems

Geologic, reservoir, and production datasets present a vast array of variables when analyzing oil & gas recovery.  What is the best approach to statistical modeling of highly complex hydrocarbon systems?  This report compares two approaches: linear multi-variant analysis and a tree-based machine learning approach.

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