• Exploring 118 wells of 1 MM+ rows and 29 columns of wireline petrophysical data using the Pandas library. • Analysed & Visualised wireline logs petrophysical dataset using Pandas, Numpy, Matplotlib, Plotly & seaborn libraries • Discovered insights of wireline logs quality & interpretation (missing data, imbalance classes and outliers problems) plus production performance.
This study explores and visualises petrophysical data of wireline logs and their subsets to analyse subsurface lithologies which describe what combination of rock layers is forming a given subsurface formation. This dataset includes 98 wells and its wireline logs, where python provides an excellent toolset for visualising the data from different views quickly and easily. We used log plots, histograms and cross plots (scatter plots) to analyse and explore well log data.
The data used for this project will be available on google drive https://drive.google.com/drive/folders/1uWz1YI6mjRm5EuJJsunOpcEdVqUaCTte?usp=sharing.
The work contained in this repositories contains work conducted during a PhD study undertaken as part of the Natural Environment Research Council (NERC) Centre for Doctoral Training (CDT) in Oil & Gas funded 50% through its National Productivity Investment Fund grant number NE/R01051X/1 and 50% by the University of Aberdeen through its PhD Scholarship Scheme. The support of both organisations is gratefully acknowledged. The work is reliant on Open-Source Python Libraries, particularly numpy, matplotlib, plotly and pandas and contributors to these are thanked, along with Jovian and GitHub for open access hosting of the Python scripts for the study.
