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Machine Learning in Oil & Gas blog

Analyzing Vista’s Record-Setting Vaca Muerta Wells with Machine Learning

4 weeks ago

Making sense of it all: extracting actionable core-data from pXRF using PCA and K-means cluster analysis

2 months ago

geoSHAP: estimating rock quality using machine learning

2 months ago

Water, water everywhere: understanding water production drivers and improving forecasts in the Williston Basin

3 months ago

The Changing Impact of Completions Through Time: URTeC Preview

3 months ago

Building Unbiased Benchmarks with Machine Learning :: URTeC Preview

3 months ago

Announcing the Novi Labs Machine Learning in Oil & Gas Blog Student Contest

4 months ago

how does a 3 billion barrel discovery turn into a $3 billion impairment?

4 months ago

the power of analogy :: using novi’s contributing well data to understand machine learning predictions

5 months ago

trust me, i’m an objective engineer: uncovering the inherent bias in oil & gas forecasts

5 months ago

first large bankruptcy filing begs the question :: is there hidden value in Whiting’s un-drilled acreage worth bidding on?

5 months ago

playing enough short game to get to the long game :: revisiting the Parsley-Jagged deal at $30 strip

6 months ago

the confidence game: optimizing completions for XEC DUC wells in the Delaware using model confidence as a proxy for risk

6 months ago

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

6 months ago

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

6 months ago

resources

Novi Water Modeling featured in JPT September Issue

Novi’s URTeC 2020 paper,  Predicting Water Production in the Williston Basin Using a Machine-Learning Model has been featured in the September issue of JPT.

The article highlights the many ways that the potent combination of machine learning models plus “explainability” datasets like SHAP values (from Game Theory!) can add value for operators, midstream companies, and local stakeholders. Digital oilfield software developed for oil forecasting can be easily adapted for water forecasting!

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

In this paper, we analyze the changing impact of interwell spacing through time on oil production in the Delaware and Midland Basins.

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

How do parent wells impact the GOR of their children?

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.

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?

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

How did the model come up with its prediction? Here we look at an example use case in the Williston Basin, using Novi’s Production Modeler to see which wells the model thought were Analogs.

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.

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