2016-ml-contest

Machine learning contest - October 2016 TLE

30-days-of-python

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.

ai_ml_seismic_log

This is for AI prediction using seismic attributes

basic-model

basic-well-log-interpretation

Basic Well Log Interpretation with python, pandas, matplotlib

brittleness-predicition-using-machine-learning

Brittleness is known to be an important reservoir property in hydraulic fracturing. Under a certain level of differential stress, brittle rocks fail creating planes of weakness that are kept open by the injected proppant, causing secondary permeability in the rock. This repo contains python scripts to estimate brittleness using elastic and mineral

classifying-geology-with-ml

cnn-for-asi

Tutorial: Convolutional Neural Networks for Automated Seismic Interpretation

counting-python

data-science-portfolio_sample

Describes all my data science experience, with links to individual projects.

ds_salary_proj

Repo for the Data science salary projection of the Data science project from scratch

em-inversion-4-buried-ice

Simple two-dimensional geophysical inversion for permafrost and ground ice detection using electromagnetic methods.

estimation-of-acoustic-impedance-from-seismic-data-using-temporal-convolutional-network

The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.

examples

Example deep learning projects that use wandb's features.

exploratory-data-analysis-with-python-and-sql

facies-classification-from-well-logs

Application of machine learning methods to the problem of facies classification from well logs (With data preproccesing)

facies-classification-using-machine-learning

faciesclassification

Facies Classification Using Machine Learning Techniques. (Logistic Regression, KNN, Support Vector Machine, Random Forest, Neural Network, Naive Bayes)

fast-times-2021-carbon-mineralization

fetal-health-classification

Phase 3 project for Data Science program at Flatiron School. Predicting fetal health outcomes using CTG data. Testing various classification models and optimizing hyperparameters with GridSearchCV. Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, Extra Trees.

geobipy

Geophysical Bayesian Inference in Python. Docs:

geohackaton_utp_petronas

Machine Learning for missing traces filling - GEOHACKATON CHALLENGE 2022

geomechanical-properties-through-ml-algorithms

The geomechanical characteristics of reservoir rock, such as Poisson’s ratio, total minimum horizontal stress, and bulk, Young, and shear modulus, are crucial factors in the present development strategies for reservoir drilling.

geophysical_notes

Collection of geophysical notes in the form of IPython/Jupyter notebooks.

geoscience-ml-papers

"Trials are medicines which our gracious and wise Physician prescribes because we need them; and he proportions the frequency and weight of them to what the case requires. Let us trust his skill and thank him for his prescription."― Isaac Newton

hamoye-internship

hamoye-internship-2021

jupyter-notebooks_for-characterization-of-a-new-open-source-carbonate-reservoir-benchmarking-case-st

We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.

learning-python-physics-informed-machine-learning-pinns-deeponets

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

lithofacies-classification-using-machine-learning

Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.