Gleipnir1's repositories
Applied-AI-ITMO-Hackathon
Hakathon on Gas and Oil + AI held in ITMO University, 05.2021
co2-data
Data on CO2 and greenhouse gas emissions by Our World in Data
Daily-Dose-of-Data-Science
A collection of code snippets from the publication Daily Dose of Data Science on Substack: https://avichawla.substack.com.
Data
In the repository you can find the data to be used during the hackathon.
Data-Science-Jupyter-Notebooks
A set of algorithms in Jupyter Notebook with datasets included
Eestec_ML_hackathon
Task was to improve rock type prediction by using labeled core and well log data for one oil and gas field with multiple horizons. We tried using Random Forest, XGBoost, Logistic Regression and SVM. Of all algorithms, when tuned Random Forest gave us the best results.
energy-data
Data on energy by Our World in Data
GeoHackathon_SPE_2021
Participated in a GeoHackathon organized by SPE in December 2021. Placed in Top Ten from 40 teams participated. Devised a development plan for a Geothermal field using subsurface and surface network data employing data driven methods and incorporating economics.
glowing-waffle
SPE Calgary Data Science Mentorship program 2021
GPN_hackathon
Hackathon of Gazprom Oil
Hackathon-Oil-Price-Prediction
There was a hackathon organized to predict the prices of Oil based on time series analysis.
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
KAUST-Hackathon-in-Geoscience
Multiphase flow in porous media governs the recovery of subsurface energy including hydrocarbon and geothermal, and their management usually requires intensive simulation runs to quantify subsurface uncertainties and optimize engineering operations, which are often expensive. In this project, we ask you to develop machine-learning-based surrogate m
Modern-Time-Series-Forecasting-with-Python
Modern Time Series Forecasting with Python, published by Packt
PetroCoder
Optimizing Rate of Penetration in Oil Drilling, Hackathon by Petrocoder, Achieved Rank 2 in Final Round(Open & Closed Dataset).
rul_codes_open
This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
S-TSFE-DL
Time Series Feature Extraction using Deep Learning
shale-gas-wells
The Korea National Oil Corporation is interested in purchasing shale gas wells from the United States and wants to predict their productions to select wells that maximize profit.
spe-production-data
SPE public production dataset
spe_geothermal_hackathon_2021
Geothermal site development plan completed as part of SPE GeoHackathon 2021
SPE_Hackathon
Juan Ceballos Hackathon Oil&Gas Algoritmos de clasificación
tsfel
An intuitive library to extract features from time series.
tsfresh
Automatic extraction of relevant features from time series:
welly
Welly helps with well loading, wireline logs, log quality, data science