Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch
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Updated
Aug 16, 2019 - Python
Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch
NeqSim is a library for calculation of fluid behavior, phase equilibrium and process simulation. This project is a Python interface to NeqSim.
Streamlit app covering our open source projects. Try it here: https://lnkd.in/g2Yr5B9
Estimate Core-based Permeability from NMR well log data
Leveraging matplotlib to visualize well drilling and construction
Calculate directional survey metadata points along the wellbore.
Python Automatic Decline Curve Analysis (DCA) For Petroleum Producing wells
Scaling solutions for production analysis from unconventional oil and gas wells
Open source Windows Application in Python for Geosciences, Artificial Intelligence and Data Sciences - Initial stages, 2021
SPE Calgary Data Science Mentorship program 2021
This repository contains a Streamlit app built to visualize and analyze energy production data from unconventional wells using a public dataset. The app allows users to interactively explore the dataset, filter data by operator, well type, and well identifier, and view visualizations of production rates over time.
Gas compressibility z-factor calculator package in Python
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A collection of Python tools for dealing with Alberta oil and gas data, including UWIs
A fast way to check and work with .dlis files.
A data prediction algorithm implemented in Python to infer depth and casing size data of California oil wells missing data using surrounding wells in the same oil field. Python libraries such as pandas for manipulating dataframes and geopy were utilized.
Code for Open-FF constructor scripts; A project to transform the industry's FracFocus disclosure data into a usable resource.
Advanced multi-segmented well completion tool
This Python script creates a Streamlit web application for a quick IPR Calculation for both an oil and gas reservoir. It collects user input test data and with it performs a curve fitting using scipy.optimize, fitting the Inflow Performance Relationship curve to the data.
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