Within the geoscience disciplines of the oil and gas industry, various formats are used to store well logging and petrophysical data. Two of the most common formats are LAS files and DLIS files. LAS files are flat ASCII files that can easily be read using any text editor, whereas DLIS files are structured binary files containing tables of information about
Radial bar charts are a visually attractive alternative to traditional bar charts. Data are plotted on a polar coordinate system instead of the conventional cartesian coordinate system. This allows the bars to be represented by rings rather than as vertical bars. Radial bar charts can make a great and visually appealing graphic to include in
Pie charts are a commonly used, easy to create circular graphic for visualising the relative sizes of different categories that contribute to a whole. Each slice within the pie chart represents a category, and its size is relative to its contribution. They are useful visualisations when dealing with a limited number of categories. Even though
Pandas is a powerful Python library commonly used within data science. It allows you to load and manipulate datasets from a variety of sources and is often one of the first libraries you come across in your data science journey. When working with pandas, the default options will be suitable for the majority of people. However, there may
Pandas is a powerful and versatile Python library for data science. It is often one of the first libraries you come across when learning how to use Python for data science applications. It can be used to load data, visualise it and manipulate it to suit the objectives of the project you are working on. However,
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
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
Neural Networks are a popular (mostly) supervised machine learning algorithm. They can be used for modelling a variety of complicated tasks such as image processing, fraud detection, speech processing, and more. These algorithms can be applied to regression-based problems as well as classification-based problems. Within petrophysics and geoscience, we can use Neural Networks to predict
Folium is a powerful Python library that makes it easy to visualise geospatial data. It utilises the power of Leaflet.js, which is a popular and leading JavaScript library that can be used to create interactive maps that can be used across both desktop and mobile platforms. The library has been designed in a way that makes it
Exploratory Data Analysis (EDA) is an important and essential part of the data science and machine learning workflow. It allows us to become familiar with our data by exploring it, from multiple angles, through statistics, data visualisations, and data summaries. This helps discover patterns in the data, spot outliers, and gain a solid understanding of the data we are working with.