k-Nearest Neighbors for Lithology Classification from Well Logs Using Python
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k-Nearest Neighbors for Lithology Classification from Well Logs Using Python

k-Nearest Neighbors (kNN) is a popular non-parametric supervised machine learning algorithm that can be applied to both classification and regression-based problems. It is easy to implement in Python and easy to understand which makes it a great algorithm to start learning about when you start your machine-learning journey. Within this article, we will cover how…

Creating a Multi-Well Integrated Well Log and Formation Tops Dataframe in Python
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Creating a Multi-Well Integrated Well Log and Formation Tops Dataframe in Python

When working with well log measurements and subsurface data we are often dealing with different file formats and sample rates. For instance, well log measurements are typically stored and transferred within .las files or dlis files and sampled every 0.1m or 0.5ft. Geological formation tops on the other hand are single discrete depth points. This requires interpolation…

Combining Formation Data With Well Log Measurements in Pandas
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Combining Formation Data With Well Log Measurements in Pandas

When working with subsurface data we often deal with datasets that have been sampled in different ways. For example, well log measurements are continuously recorded over intervals of the subsurface at regular increments (e.g. measurements every 0.1 m), whereas formation tops are single depth points. Within this article, we will cover a way of integrating…

6 Python Libraries You Should Know About for Well Log Data & Petrophysics
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6 Python Libraries You Should Know About for Well Log Data & Petrophysics

Discover 6 great python libraries that you can start using today with well log data One of the great things about Python is the vast number of open source libraries that have been developed to improve the way we work with data and make sense of it. Within the petrophysics and geoscience domains, there are a…

Enhance Your Plotly Express Scatter Plot With Marginal Plots
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Enhance Your Plotly Express Scatter Plot With Marginal Plots

Scatter plots are a commonly used data visualisation tool within data science. They allow us to plot two numerical variables, as points, on a two dimensional graph. From these plots, we can understand if there is a relationship between the two variables, and what the strength of that relationship is. Within this short tutorial, we…

Using Plotly Express to Create Interactive Scatter Plots
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Using Plotly Express to Create Interactive Scatter Plots

Scatter plots allow us to plot two variables from a dataset and compare them. From these plots, we can understand if there is a relationship between the two variables, and what the strength of that relationship is. Within petrophysics scatter plots, are commonly known as crossplots. They are routinely used as part of the petrophysical…

Displaying Logging While Drilling (LWD) Image Logs in Python
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Displaying Logging While Drilling (LWD) Image Logs in Python

Borehole image logs are false-color pseudo images of the borehole wall generated from different logging measurements/tools. How borehole images are acquired differs between wireline logging and logging while drilling (LWD). In the wireline, environment measurements are made from buttons on pads that are pressed up against the borehole wall and provide limited coverage but at…

Petrophysics: Gamma Ray Normalization in Python
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Petrophysics: Gamma Ray Normalization in Python

Normalization of well log data is a common and routine process within a petrophysical workflow and is used to correct for variations in logging curves between wells. These variations can arise due to a number of different reasons such as incorrect tool calibrations, varying tool vintage and changes in borehole environmental conditions between the wells….