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Machine Learning

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

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Petrophysics

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

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Petrophysics

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

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Essential Soft Skills to Succeed as a Data Scientist
Data Science

5 Essential Soft Skills to Succeed as a Data Scientist

Data science is a rapidly growing field with the demand for data scientists projected to grow by 36% over the next decade according to the US Bureau of Labor Statistics. However, the field can be very competitive, and there are many steps between being an entry-level data scientist and getting promoted into a senior role at a large company.

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Machine Learning

How to Create a Simple Neural Network Model in Python

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

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Data Science

Folium Mapping: Displaying Markers on a Map

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

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Data Science

Data Quality Considerations for Machine Learning Models

Reducing the Impacts of Garbage In Garbage Out on Machine Learning Models Ensuring you have good data quality prior to running machine learning algorithms is a crucial step within the overall data science and machine learning workflow. The use of poor-quality data can lead to severe degradation in the results and have further consequences when

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Image by Reto Scheiwiller from Pixabay
Machine Learning

6 Machine Learning Algorithms to Know About When Learning Data Science

Machine Learning has become very popular over the past decade or so with many industries adopting new algorithms to automate processes and increase productivity. Machine Learning is a branch of Artificial Intelligence (AI) and involves machines/computers learning from data and generating results without being explicitly programmed to do so. In traditional programming, we provide the input data and

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Petrophysics

Reservoir Rock Typing Definitions

Using well log data to subdivide and group the subsurface into meaningful packages is a common task within geoscience and petrophysics. It allows log responses and rocks that exhibit similar responses and characteristics to be grouped together into what is known as facies. Once they are grouped together they can be used for further analysis,

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Python

Pandas Profiling — Easy Exploratory Data Analysis in Python

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.

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