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

“What do you mean the model doesn’t use **INSERT PET GEOLOGIC VARIABLE HERE**?!” Anyone who’s built and reviewed enough machine learning models and SHAP values will instantly recognize the question above. The algorithm dropped some geologic variable in the feature selection process due to high correlation with another input geologic variable, and someone in the […]

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 oil well 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. […]

how does a 3 billion barrel discovery turn into a $3 billion impairment in oil and gas plays?

Involvement in exploring a substantial discovery and bringing it to development is an exciting opportunity. There is nothing more valuable to a company than taking an unknown prospect, acquiring the land for low cost, and turning it into a cash generating machine. Finding such an oil and gas play is the motivation for many teams. […]

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. We use geologic and engineering similarities to group oil and gas wells, then we use an average of that group to […]

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

Introduction: Problem we are trying to solve; methodology we are applying to solve it We have written a couple of posts about optimizing completions on DUC (Drilled but UnCompleted) well inventory given the extreme constraints on capital that Operators are facing. In our first post, we focused on QEP’s inventory in the Midland, and then […]

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

Given the extreme capital constraints imposed by a lower commodity price environment, many operators have announced CAPEX reductions and significant rig layoffs. Given these reductions, it would make sense to apply what capital remains to complete through DUC (Drilled, but UnCompleted) wells. If this is true, it would also make sense to revisit the completion […]

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