Novi Labs

Big Data Management

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

WHY CARE ABOUT PXRF DATA? Thanks to relatively short scanning times and low operating cost, portable X-ray Fluorescence (pXRF) scanning of geologic core samples has become a burgeoning big data in oil and gas industry, with the technique capable of generating high resolution datasets containing comprehensive major and trace element abundances present in the rock.

We will be using Scikit-learn, an open source machine learning library that supports supervised and unsupervised learning towards a variety of applications. Here we will be utilizing principal component analysis and k-means clustering algorithms to manipulate and segment the big data before visualization.

The process outlined in this blog post develops labeled datasets that can be tied to the core, ahead of the implementation of a neural network. Read the full post here.

Novi geoSHAP – estimating rock quality using SHAP values in machine learning models: URTeC 2020 Novi Paper Summary

Can a machine learning model learn where the play sweet spots are, just from raw well logs? The answer is YES. Using logs (or any input geo variables), plus a principal components analysis, gives the model everything it needs to learn what drives production.

This is the subject of our most requested URTeC 2020 paper — geoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis. Click through to learn more!

Water, water everywhere: oil well water analysis with machine learning models to improve produced water forecasts in the Williston Basin: URTeC 2020 Novi Paper Summary

How often does an engineer dash off a simple produced water analysis, doing something like applying a flat WOR to their oil prediction? It’s easy to ignore water, but unexpectedly high production, leading to more produced water, can damage well economics. In the worst cases, it can force shut-in if disposal capacity is full.

Fortunately, advanced machine learning methods developed for oil can be applied to water. These technique help disentangle the complex interactions of completions, geology, and spacing.

This is the subject of our URTeC 2020 (and JPT-featured) paper. Read the summary here.

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

Whether exploring for oil offshore Brazil or scaling type curves in Lea County, engineers and geologists rely upon the power of analogy to estimate the productivity of a given area or engineering design choice. Tree-based machine learning models can quantify similarity based on what matters: how it contributes to production.

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

When Parsley Energy announced their acquisition of Jagged Peak in October 2019, WTI was trading at $55/bbl. Though the all-stock nature of the transaction protected their downside, Parsley still faced the question: “how can we economically develop the Jagged Peak acreage?”

In this post, we study completions and spacing options to maximize acreage value. Click through to read our analysis.

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

Optimizing DUC inventory is something that many operators unfortunately have to wrestle with after pauses in completions activity.

Machine learning models can help you design the right completions for each well — and Lead-in sentence then understand the risk and uncertainty to prioritize your capital allocation.

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