Introduction:
This comprehensive Course is designed for Engineers & New Learners who want to learn how to use Python to solve problems in the Oil and Gas industry. The Course will teach you the core concepts of Python and how to apply them to real-world problems in Oil & Gas.
The course will begin with an introduction to Python and programming basics, followed by an in-depth look at lists, dictionaries, and control flow statements. You will also learn how to use NumPy and Pandas libraries for data manipulation and analysis, and Matplotlib for data visualization.

The second part of the course will focus on applying Python to Engineering problems. You will learn how to create material balance and inflow performance relationship (IPR) curves using Python, which are essential for understanding reservoir engineering and estimating oil and gas reserves and production rates. The final part of the course will focus on deployment. You will learn how to deploy a Python web application that demonstrates your ability to create a user-friendly interface for accessing Oil and Gas data. This will give you a practical understanding of how to create and deploy applications using Python.

Learning Objectives:
By the end of this course, you will have a solid foundation in Python and be able to apply your skills to real-world problems. You will also have the practical skills necessary to create and deploy a Python web application for accessing Oil and Gas data.

Duration: 25+ hours

Prerequisites: This training is ideally designed for all Students & Professional, it is not a pre-requisite, and every attempt shall be made to include people from other educational backgrounds.

Topics to be covered:

A. Python Fundamental

Module 1:

• Why Data Science/ AI ML in Oil & Gas.
• Introduction to python
• IDE (Integrated development environment)
• Installations

Module 2:

• First python program
• Data Types in python
• Variables in python
• Mathematical Operations

Module 3:

• Introduction to data structures
• Lists: storing data
• Tuples
• Dictionaries

Module 4:

• If-else condition blocks
• While Loop
• For loop
• Iterables

B. Python Intermediate and Advance, Introduction to GIT

Module 5:

• Functions: Reduce your work
• Making your own python modules
• Exception Handling
• Git: Integrating git in your local

C. Data Analytics Libraries

Module 6:

• NumPy: Numerical Python
• Arrays
• Reshape, resize, playing around the data
• Synthetic Data Generation

Module 7:

• Pandas: read, process, manipulate tabular data
• Data Frames
• Introduction to Data Visualization: Matplotlib

Module 8:

• Hands on python mini projects: Pressure Profile, Klingenberg Effect
• Deployment of application on cloud server