Jydon's repositories
tutorial_ml_gkbionics
A Tutorial on Simple Machine Learning Methods Held for the Graduate School on Bionics, 2012
FEM-Reservoir-Drainage-3D
Compares analytical/numerical results for the drainage of a single well.
IBM-Data-Science-Tools-Assignment
This is python notebook for Data Science Tools (Module 4 - Final assignment)
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
PEG_Python
Petroleum Engineering and Geosciences exercises in Python
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.
pydca
A "simple" decline-curve analysis example in Python
Python
This repository helps you understand python from the scratch.
typecurve.js
typecurve.js: some tools for decline-curve analysis and aggregation on the client side