augustaya's Stars
seg/2016-ml-contest
Machine learning contest - October 2016 TLE
bolgebrygg/Force-2020-Machine-Learning-competition
the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.
petroGG/Basic-Well-Log-Interpretation
Basic Well Log Interpretation with python, pandas, matplotlib
andymcdgeo/Andys_YouTube_Notebooks
petroGG/petrophysics
A python package with useful functions for well log interpretation.
Philliec459/Geolog-Python-Loglan-use-of-Altair-to-Interrogate-Log-Analysis-data
The objective of this repository is take advantage of Geolog's powerful python loglan capabilities to interrogate Petrophysical well log data using python's interactive Altair in Geolog. The use of python in Geolog will allow us to leading-edge data science techniques in Geolog to process, interrogate and interpret out logs.
f0nzie/evolution_data_science_petroleum_engineering
Evolution of data science, machine learning and artificial intelligence in Petroleum Engineering papers
RamySaleem/Exploratory-Data-Analysis-of-Wireline-Well-Log-Data
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 and imbalance class
BhavarthShah/Well-log-dataset-interpretation
Demonstrates how conveniently the interpretation of well-logs can be done using Python
andymcdgeo/seaborn_tutorial_series
johnodonnell123/Personal_Projects
KarlOstradt/Master-2021-Hybrid-Human-Machine-interpretation-of-well-logs-using-deep-learning
Amosmeng/End-to-End-Machine-Learning-Project
Leveraging machine learning to predict reservoir rocks in an oil and gas field offshore Norway. This project was completed in October 2018 and it was built upon work in the following notebook https://github.com/seg/2016-ml-contest/blob/master/Facies_classification.ipynb © 2019 GitHub, Inc.
Vardhan-Petrophysicist/Poseidon_Well_Data_Adaptive-Boosting_ML
Adaptive Boosting of Poseidon Well Data using Random Forest. The Poseidon-2 well was used in training and tested on the other wells. The dataset is publicly available as part of the Creative Commons License.
anderalex803/SPE_NAICE-Reservoir-Facies-Classification
This study employed formation samples for facies classification using Machine Learning techniques and classified different facies from well logs in seven (7) wells. The log data were trained using supervised machine learning algorithms to predict discrete facies groups. The analysis started with data preparation and examination where various features of the available well data were conditioned.
pkhetland/Facies-prediction-TIP160
Training "Boosted Trees"-models and CNNs to predict facies type based on seismic data. Underlying Kaggle-competition: https://github.com/seg/2016-ml-contest
Vardhan-Petrophysicist/Vardhan-Petrophysicist
Config files for my GitHub profile.