Evgeneom's repositories
Machine-Learning-for-Petroleum-Engineering
Python notebooks for applications of Machine Learning algorithms in Petroleum Engineering (Reservoir, Well-Logs, Completions, etc).
dynamic-reservoir-characterization
An useful tool for pressure transient analysis.
Pressure_Transient_Analysis
Python Based Pressure Transient Analysis (PTA)
scipy
SciPy library main repository
xlwings
xlwings is a Python library that makes it easy to call Python from Excel and vice versa. It works with Excel on Windows and macOS as well as with Google Sheets and Excel on the web.
Basic-Well-Log-Interpretation
Basic Well Log Interpretation with python, pandas, matplotlib
scipy-cookbook
Scipy Cookbook
practical-statistics-for-data-scientists
Code repository for O'Reilly book
The-Data-Visualization-Workshop
A New, Interactive Approach to Learning Data Visualization
The-Data-Science-Workshop
A New, Interactive Approach to Learning Data Science
pragmaticai
[Book-2019] Pragmatic AI: An Introduction to Cloud-based Machine Learning
scipy-lecture-notes
Tutorial material on the scientific Python ecosystem
statsintro_python
Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"
Stats-Maths-with-Python
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
CNN-for-ASI
Tutorial: Convolutional Neural Networks for Automated Seismic Interpretation
Statistics-for-Data-Science
Statistics for Data Science, published by Packt
python-for-data-science
Осторожное введение в исследование данных / A Gentle Intro to Data Science
Data-Statistics-with-Full-Stack-Python
Data Statistics with Full Stack Python, published by Packt
Statistics-for-Data-Science-and-Business-Analysis
Statistics for Data Science and Business Analysis, published by Packt
Statistics-for-Data-Science-Video
Statistics for Data Science by Packt Publishing
Statistics-for-Machine-Learning
Statistics for Machine Learning, published by Packt
PTA-in-Python
Collection of codes for Pressure Transient Analysis
Oil-Output-Modeling
A close comparison of three different regions for potential oil development. Here I was tasked with taking proprietary data about three distinct regions and advising on which region would produce the highest ROI. This involved building a logistic regression model and testing the findings against bootstrapped data to ensure significantly accurate results. From this, I was able to make a recommendation on the best region for further oil development. This was project 8 of Practicum by Yandex.
DeepLearning-NLP
Introduction to Deep Learning for Natural Language Processing
MalenoV
Complete tool for training & classifying facies on 3D SEGY seismic using deep neural networks