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cnn-lstm-network-architecture-for-geological-mapping-with-geochemical-exploration-data

Application of convolutional recurrent neural networks for geological feature mapping based on geochemical exploration data.

cnn_3d_permaebility

This research is about predicting permeability of the 3D rock images using Convolutional Neural Network.

cnn_microseismic

Deep learning on microseismic data

codedatascience

combined_nmr_conventional_logs-shaley-sand-analysis-using-petrogg

Mihai's PetroGG modified to be used with our shaly-sand Gulf Coast NMR data.

cp_datathon

This is a machine learning model that predicts oil well failure.

create-thin-section-image-labels-for-semantic-segmentation-training

This is an example of creating labeled images to be used in Keras image-segmentation training

cs394r_rl_directional_drilling

Course Project for CS394R Reinforcement learning -- Practice and Theory

cspg_practical_programming_for_geoscientists

cvx

useful snippets

dasprodml

A reader and a writer for the prodml standard

data-analysis-using-python

data-science-template

A starter template for Equinor data science / data engineering projects

datasciece-for-petroelum

using data science tools for petroleum industry

deep-learn-oil

Deep learning tools for predicting oil well data

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-week

This 5 day online course was co-organised by LRZ and NVIDIA Deep Learning Institute (DLI), combined lectures about Fundamentals of Deep Learning for Single and for Multi-GPUs, Building Transformer-Based Natural Language Processing Applications and Deep Learning on LRZ systems.

deep-neural-network

Deep Neural Network and other ML algorithms are developed to predict damage and oil outflow in tanker collision accidents https://doi.org/10.1177/14750902211039659

deep_ml_curriculum

Data and Machine Learning Curriculum for Oil, Gas, and Mining

deeplabv3_pores-grains

DeeplabV3+ Model for semantic segmentation of pores and grains of mudrock SEM images.

deeplearning.ai-summary

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

direct

Digital Reservoir Characterization Technology Consortium, UT Austin

downdyn

Virtual Machine Image for DownDyn from Sandia

ds_portfolio

Katherine's Data Science Portfolio

dsi_summer_workshops

dutch-housing-market

eagleford-oil-project

Analysis of Eagle Ford Shale Assets using Python

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.

efacies

Multiscale electrofacies analysis on borhole data

elastic-impedance-inversion-using-recurrent-neural-networks

Codes and data for a manuscript in Interpretation Journal, Aug 2019