There are 21 repositories under oil topic.
multi-shell multi-command argument completer
command argument completion generator for spf13/cobra
Enverus Drillinginfo Direct Access Developer API Python Client
Script for accessing and organizing oil and gas well data from the Texas Railroad Commission
Machine Learning to predict share prices in the Oil & Gas Industry
Risk tools for commodities trading and finance
Data and Machine Learning Curriculum for Oil, Gas, and Mining
lazy loading for shell completion scripts
An R package for reading Log Ascii Standard (LAS) files for well log data.
3D Graphical Output of well producers and injectors in oil reservoir.
define simple completions using a spec file
Offshore drilling platforms near the coast of Nigeria. Accessible at https://doubleoffshore.org/
NeqSim is a library for calculation of fluid behavior, phase equilibrium and process simulation. This project is the Matlab interface to NeqSim.
Analyze your consumption of electrical power, fresh water, heating oil, gas, pellets, heat pump energy, and firewood mass with python
completion bridge
With massive amount of permutations possible when scheduling Oil & Gas PAD development, an automated optimizer is essential in order to identify the best development sequence. This repository allows to honour development hard constraints and assess performance of soft constraints.
A website developed for a coconut oil manufacturing company
Crude/Product cargo calculation for Oil/Product tankers according to the American Society for Testing and Materials (ASTM)
The EnviroGas app and database are being designed to track and monitor CO2 and methane emissions in the oil and gas sector.
In this project, we have developed an IoT device to analyze the transformer oil without shutting down the grid. By creating this device, the oil sampling and analysis process is done automatically and no longer need to cut off the grid.
Repository for the second of two capstone projects
Example applications of GDELT mass media intelligence data
Oil & Gas tool to estimate Original Resources in Place (OOIP OGIP) from a group of Surfaces.
Implemented machine learning algorithms to classify the types of alarm of corrosion in the pipeline. The performance of each model was compared and analyzed using Accuracy and Confusion Matrix.