About this Playlist

In this tutorial, we will start with basic Python programming and explore its applications in our domain. We will cover:

• Pandas: Data manipulation and processing
• Matplotlib and Seaborn: Data visualization
• Decline Curve Analysis (DCA): Understanding ARP's model for conventional reservoirs
• DCA with Python: Building a simple ARPS module
• Streamlit: Creating a web application for DCA

Videos

#	Title	Summary
1	About The tutorial	What to expect from this tutorial, what are the prerequisites
2	Pandas Basics	Going through pandas basics like ( reading data , data manipulation , data filtering , grouping )
3	Task 1	Solve a simple task about pandas using volve production dataset
4	Data visualization | Part 1	Matplotlib main object and all graph components using line chart
5	Data visualization | Part 2	bar chart , pie chart and subplots
6	Data visualization | Part 3	Interactive plots using ipywidgets (interact), using seaborn for better visualization
7	Data visualization | Part 4	Distribution plots , PDF , KDE and plotting using pandas and customize the chart.
8	DCA | ARPS' Model	What is ARPS model for conventional reservoirs.ARPS theory , assumptions and limitations
9	Data Smoothing | Moving Average	Smoothing produciton data using moveing average technique for better fitting.
10	DCA ARPs Model With Python	Using volve produciton dataset, apply data smoothing , curve fitting, getting model parameters and forecast.
11	ARPS Module | Part 1	Convering the plain code of ARPS using python into module part 1
12	ARPS Module | Part 2	Convering the plain code of ARPS using python into module part 1
13	ARPS DCA Web APP | Streamlit	Now it is time to make our first simple GUI App with python. convert all our work into a simple web app using Streamlit that require no web development experience at all.

About this Playlist

This tutorial is just an intro to machine learning , what is ML and it's types with exploring common ML algorithms and a simple Example in PE.

Videos

#	Title	Summary
1	What is ML	An into about ML, Why ML , What is ML, ML Types, ML process and Types.
2	Supervised & Unsupervised ML Examples	Showing two exmaples of ML ( Image Classification , Dimensionality Reduction ) using google palyground that is accessible for anyone.
3	ML Workflow	Different steps of ML ( Data colleciton , Data Processing , Model training , Model Evalulations ... )
4	ML Optimization Process	How the model is trained and optimized using gradient descent ( Linear Regression )
5	Linear Regression Example	prediction the flow capacity for unconventional reservoirs ( Example from ML for oil and gas industry book).
6	Logistic Regression and Classification Metrics	What is Logistic Regression and the math. what are the different Classificaiton metrics ( Accurary , Precision , Recall ) and Confusion Matrix.
7	Classificaiton Example	Using HR Dataset showing how to build a simple classification example ( training and evaluating the model )
8	Overfitting Vs Underfitting	what do overfitting and underfitting means, how to deal with them. what is the tradeoff between them.
9	Enhance Generalization | Regularization	Regularization for the problem of overfitting using L1 ,L2 methods [ lasso, ridge ]
10	KNN algorithm	classification problem with KNN, predict whether the employee will quit or not
11	Hyperparameters Tunning | Cross Validation	know what is hyperparameters, hyperparameter tunning ,grid search and random search, kfolds cross validation
12	Decision Tree & Random Forrest	This video explain the intuition behinde the Decision tree algorithm , how it is used for both- classificaiton and regression. - the Random Forrest algorithm and the bagging technique
13	Lithology Classificaiton with Random Forrest	Using Force-2020 dataset to predict the lithology using logging data ( gamma ray , density , neutron , resistivity ....)
14	Extra Trees And Random Forrest	Showing the most used model in ML which is ( Extreem gradient boosting XGB ) moded.
15	Support Vector Machine SVM	Understand the inuition behinde the SVM for binary Classificaiton.