Olutoki John's repositories
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.
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.
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.
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)
machine-learning-1
Practicing machine learning (from scratch) with Python 🐍
MachineLearningCourse
My graduate level machine learning course, including student machine learning projects.
mineral-exploration-machine-learning
This page lists resources for mineral exploration and machine learning, generally with useful code and examples.
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
MLfromscratch
Machine Learning algorithm implementations from scratch.
NEW-Carbonate-Characterization-Workflow-Jupiter-Notebook-Modules-with-Clerke-Arab-D-Calibration-Data
Carbonate Reservoir Characterization workflow using Clerke’s carbonate Arab D Rosetta Stone calibration data to provide for a full pore system characterization with modeled saturations using Thomeer Capillary Pressure parameters for an Arab D complex carbonate reservoir
OlutokiJohn
Config files for my GitHub profile.
open_petro_elastic
Utility for calculating elastic properties of petroleum fields
PEIP
MATLAB code for examples and exercises for the 3rd edition of Parameter Estimation and Inverse Problems
Petrophysics-Python-Series
A series of Jupyter notebooks showing how to load well log and petrophysical data in python.
practical-seismic-t21-tutorial
T21 tutorial using Volve data
pythondataanalysis
Python data repo, jupyter notebook, python scripts and data.
PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Seismic-Facies-Analysis-DCAE
Unsupervised seismic facies analysis via deep convolutional autoencoders.
seismic_deep_learning
A couple of python scripts to interpret geological structures from geophysical images using deep learning
seisproc
Some simple and useful seismic processing routines.
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.
Velocity_prediction
Velocity is one of the most important petrophysical parameters used in oil-field optimization or other geophysical surveys to easily determine and predict horizons and other features.
volve-machine-learning
Exploration of machine learning in the Volve field dataset