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.
Quick and simple conversion process to create a LAS file from scratch using LASIO and CSV Data Well log data can be delivered in a variety of formats (DLIS, LAS, CSV, ASC etc.). If you have a CSV file containing well-log data, and you want to convert it to LAS format, you can use the LASIO…
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…
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…