liweirong1120 Goto Github PK

machine-learning-competition-2020

My personal work on the 2020 SPWLA PDDA Sonic Prediction Competition

machine-learning-competition-2021

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

machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization

machine-learning-for-physicists

Code for "Machine Learning for Physicists" lecture that I joined in 2020

machine-learning-oil-gas-industry

Source Code for 'Machine Learning in the Oil and Gas Industry' by Yogendra Narayan Pandey, Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, and Luigi Saputelli

mlcourse.ai

Open Machine Learning Course

mlstm-fcn

Multivariate LSTM Fully Convolutional Networks for Time Series Classification

montepetro

Monte Carlo estimation of reservoir reserves.

ms_net

The MultiScale Network for hierarchical regression (MS-Net) performs 3D regression based on a hierarchical principle: coarse inputs provide broad information about the data, and progressively finer-scale inputs can be used to refine this information.

named-entity-recognition-bidirectionallstm-cnn-conll

Keras implementation of "Few-shot Learning for Named Entity Recognition in Medical Text"

nechi-reservoir-network

This repository contains Matlab code describing a operational model of the reservoir network in the Nechí catchment, Colombia, and a behavioural component simulating operational strategies depending on 12 parameters. The codes are directly related to the publication (in submission): "Multi-objective optimization of physically based operational rules for multi-reservoir systems" - J. Ritter, G. Corzo, D.P. Solomatine, H. Angarita (2017)

neo4j

Graphs for Everyone

numpy-

开源的测井数据集 Open source well logging data set

open-geoscience-repository

Open geoscience datasets available in open databases from Google Drive, SEG Wiki, and US DoE Geothermal Data Repository OpenEi

openlbmpm

openLBMPM is an open source lattice Boltzmann method (LBM) package for multicomponent and multiphase (MCMP) flow and transport in porous media. Currently, it includes Shan-Chen method and color gradient method for MCMP system. There are two options for Shan-Chen method: (1) Original Shan-Chen method, which integrates the force term to the equilibrium velocity and cannot reach high viscosity ratio; (2) Explicit forcing model developed by M.Porter et al (2012). For color gradient model, the methods developed by Liu et.al (2014), Huang et al (2014) and Takashi et al (2018) are included. For running the codes, CUDA and numba (from Anaconda) are required

orthogpolykans

Kolmogorov-Arnold Networks (KAN) using orthogonal polynomials instead of B-splines.

pdda

SPWLA PPDA 2020

petrogg-modified-for-shaly-sand-interpretation

We have used Mihai's PetroGG and modified the program to be used with our shaley-sand Gulf Coast data. In this version we are using Vshale and not Vclay, and we have added Waxman-Smits and Dual-Water saturation models appropriate for these data.

petroleum-data-analytics-series

Series of tutorials and codes for Petroleum Data Analytics & ML

petroleum-production-prediction

The final project at university using machine learning and deep learning to predict oil and gas production

petrophysics-python-series

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

piml

My pytorch based implementation of the paper 'Limitations of Physics Informed Machine Learning for Nonlinear Two-Phase Transport in Porous Media'

pinns

PyTorch Implementation of Physics-informed Neural Networks

pinns-tf2.0

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

pipeline_corrosion_cnn

playground

ES-MDA for updating parameters based on data conditioning

popular-rl-algorithms

PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..

poreflow-net

3D CNN to predict single-phase flow velocity fields

predicting-hydrogen-storage-in-nanoporous-materials-using-meta-learning

Predicting hydrogen storage in nanoporous materials using meta-learning

progressive_growing_of_gans

Progressive Growing of GANs for Improved Quality, Stability, and Variation