ugur-yuce

ugur-yuce's Stars

• base-org/chains

  provides metadata for networkIDs and chainIDs

  Language:Kotlin79.5k39502.5k

• Developer-Y/cs-video-courses

  List of Computer Science courses with video lectures.

  68.4k1.9k1459.3k

• plotly/plotly.py

  The interactive graphing library for Python :sparkles:

  Language:Python17k2763.1k2.6k

• jonasschmedtmann/complete-javascript-course

  Starter files, final projects, and FAQ for my Complete JavaScript course

  Language:JavaScript16.3k91718317.2k

• smartcontractkit/full-blockchain-solidity-course-js

  Learn Blockchain, Solidity, and Full Stack Web3 Development with Javascript

  13.2k2582523.2k

• LayerZero-Labs/LayerZero-v2

  Language:Move6262969384

• agilescientific/welly

  Welly helps with well loading, wireline logs, log quality, data science

  Language:Python32840194136

• andymcdgeo/Petrophysics-Python-Series

  A series of Jupyter notebooks showing how to load well log and petrophysical data in python.

  Language:Lasso313345150

• itublockchain/web3-bootcamp

  Open-Sourced Turkish Smart Contract Developer Bootcamp by ITU Blockchain

  Language:Solidity28617072

• agilescientific/bruges

  Bag of really useful geoscience equations and stuff

  Language:Python2844971116

• permaweb/ao

  The ao component and tools Monorepo - 🐰 🕳️ 👈

  Language:JavaScript2251445274

• agilescientific/striplog

  Lithology and stratigraphic logs for wells or outcrop.

  Language:Python2102514168

• Python-Fuzzylogic/fuzzylogic

  Fuzzy Logic and Fuzzy Inference for Python 3

  Language:Jupyter Notebook13976327

• equinor/dlisio

  Python library for working with the well log formats Digital Log Interchange Standard (DLIS V1) and Log Information Standard (LIS79)

  Language:Python1281017243

• LayerZero-Labs/devtools

  LayerZero Endpoint V2 Examples and Developer Tooling

  Language:TypeScript1211643188

• ruesandora/stafihub-testnet

  StaFiHub validator testnet guide

  10130163

• forta-network/forta-node

  Scan Node software for the Forta Network

  Language:Go816229151

• pddasig/Machine-Learning-Competition-2020

  SPWLA PDDA’s 1st Petrophysical Data-Driven Analytics Contest -- Sonic Log Synthesis

  Language:Jupyter Notebook6812460

• sede-open/Core2Relperm

  Core2Relperm project for inverse modelling of core flooding experiments

  Language:Jupyter Notebook596214

• brendonhall/facies_classification

  Language:Jupyter Notebook567445

• pro-well-plan/petrodc

  Petroleum Data Collector

  Language:Python517012

• yohanesnuwara/formation-evaluation

  Python utility for formation evaluation and petrophysical analysis support

  Language:Lasso403023

• salmansust/Machine-Learning-TSF-Petroleum-Production

  Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy

  Language:Python330117

• MattJones82/Modeling-NMR-Derived-T2-Porosity-using-Conventional-Open-Hole-Logs

  Machine Learning Modeling of NMR-Derived T2 Porosity using Quad Combo, Wolfcamp and Spraberry Formations, Reagan County Texas

  Language:Lasso11101

• UW-MLGEO/MLGeo-2023

  MLGeo-2023 Machine Learning for the Geosciences at UW

  Language:Jupyter Notebook10108

• Chrisaranguren/Petrophysical_Interpretation_Random_Forest_Regression-

  The goal of this article is to follow a recommended machine learning workflow on how to perform a petrophysical interpretation using an ensemble technique (Supervised Learning model), which is widely known as Random Forest Regression.

  Language:Jupyter Notebook9102

• brendonhall/ML

  Language:Jupyter Notebook4002

• AlmazErmilov/Applying-Machine-Learning-NLP-Algorithm-for-Reconciliation-Geology-and-Petrophysics-in-Rock-Typing

  Language:Jupyter Notebook2200

• Amirasrour/Machine-Learning-in-Petrophysics-Random-Forest-Models-

  Random Forest Regressor For Predicting Continuous Well Measurements and Random Forest Classifier For Lithology classification

  Language:Jupyter Notebook210

• Amirasrour/Machine-Learning-in-Petrophysics-

  KMeans clustering

  Language:Jupyter Notebook1