This intensive training program equips reservoir and production engineers with the practical Python skills needed to analyze data, optimize production, and make informed decisions. Gain hands-on experience applying Python to real-world engineering challenges.

Learn fundamental Python programming concepts relevant to reservoir and production engineering

Master data analysis and visualization techniques using Python libraries

Apply Python to reservoir modeling, production optimization, and well performance analysis

Develop practical skills through hands-on exercises and projects

Expert-led instruction with a focus on practical application

Interactive sessions with ample opportunities for Q&A

Hands-on exercises and projects using real-world data

Collaborative learning environment

Enhance engineering team's capabilities and efficiency

Improve decision-making through data-driven insights

Optimize reservoir and production performance

Drive innovation and cost savings

Gain valuable, in-demand skills

Boost your career prospects

Expand your professional network

Stay ahead in the rapidly evolving energy industry

Reservoir and Production Engineers

Petroleum Engineers

Geoscientists

Data Scientists and Analysts in Energy

Python for Production Module

Day 1

Introduction to Python basics

Python IDEs and available tools

Python Data Structures

Loops and Branching.

Introduction to Tabular data Using pandas

Project 1: Calculating Production Parameters from Tabular Data using Pandas

Day 2

Introduction to Plotting Using Matplotlib

Introduction to plotting using Plotly

Project 2: Calculating, Visualizing, Exporting Fluid Properties.

Project 3: Calculating the Heterogeneity index for Oil Wells.

Introduction to Lasio library for Well Log files.

Day 3

Project 4: Well Log Interpretation and Visualization using Python

Introduction to IPM PVTp OpenServer Commands

Introduction to IPM MBAL OpenServer Commands

Project 5: Controlling IPM package using DO, SET, GET and Python

Day 4

Introduction to Streamlit dashboarding and the visual components

Introduction to Curve Fitting.

Project 6: Water Production Diagnostics via Chan Plot.

Project 7: Grid, and heat Mapping for Reservoir Production and Petrophysical Properties.

Day 5

More on data Manipulation and Advanced pandas.

Introduction to Statistics and Distribution

Introduction to fast Dashboarding using Gradio.

Project 8: Carlo Simulation and Histograms for OOIP

Python for Reservoir Engineering and Surveillance Module

Day 6

Introduction to Python basics

Python IDEs and available tools

Python libraries and PIP command

Use Case 1: Inflow Performance Relationship

Introduction to plotting

Use Case 2: Create Interactive Production Plots

Day 7

Handling tabular data using Pandas

Using Plotly for Interactive data visualization

Use Case 3 : Create a Composite Production Plot with Multiple Y Axis

Liquid Loading Calculation in Python.

Use Case 4: Turner’s & Colman’s Rate for Gas Well Diagnostics

Day 8

Python automation and Integration

Introduction to PETEX GAP.

Introduction to GAP API.

DO, SET, GET commands in GAP and Open Server Integration with Python

Use Case 5: Connecting GAP to Python for various tasks.

Day 9

Introduction to Dashboarding with Streamlit

Use Case 6: Create a Production Dashboard for Oil and Gas Fields

Introduction to Well Integrity and Barlow Equation.

Introduction to Lasio library for Well Log files.

Use Case 7: Calculated MAOP from MultiFinder logs(MFC)

Day 10

Use Case 8 : Converting WHP to BHP using Beggs and Brill Correlation

Use Case 9 : Operational Data Smoothing using various Filters and AVG algorithms

Use Case 10: Automated Decline curve analysis (Time-Rate)

On successful completion of this training course, PEA Certificate will be awarded to the delegates