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Some time ago, I started a collaboration with Alan at CrowdField, where he provided guidance and support for me to develop my first Streamlit app.
I was eager to learn how to use Python to build simple, user-friendly applications that run in a browser and have real-world applications in the oil and gas industry. After discussing several options with Alan, I decided to take on the challenge of building an interactive interface for relative permeability curves.
The process and dynamics of our collaboration were described in a blog post Alan published on the CrowdField website:
Case Study: Collaborating with a Young Engineer to Develop a Streamlit Relative Permeability App
Here’s a short YouTube demo showcasing an app run-through:
And here’s a link to the Streamlit app itself, which you can run in any browser:
👉 Link to Rel Perm StreamLit App
So, I decided to dive deeper into the technical aspects and create a detailed, step-by-step guide on how to develop a Streamlit app like this one.
My goal is to encourage others to explore this powerful tool.
When we decided to implement a Streamlit app for relative permeability curves, we envisioned an interface that would:
Here are the steps to create a Streamlit app.
To get started, you need Python installed on your system along with the necessary libraries.
python --versionYou should see an output like Python 3.x.x.
Create a virtual environment to isolate dependencies and install necessary libraries.
Open your terminal or command prompt and navigate to your working directory:
mkdir streamlit_app
cd streamlit_app
Create and activate a virtual environment:
python -m venv env
env\\Scripts\\activate# Windows
source env/bin/activate# Mac/Linux
Install the required libraries:
pip install streamlit numpy pandas plotly openpyxl requests PyPDF2
Inside your project folder, create a new Python file:
touch Myapp.py
Open Myapp.py in your preferred text editor (e.g. VSCode) and add the following imports:
import streamlit as st
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from PyPDF2 import PdfReader
Functions serve as the backbone of the application, each dedicated to a specific task such as calculating permeability, exporting data, or generating plots.
For relative permeability calculations, I've implemented Corey functions, using exact formulas sourced from the references listed at the end of this guide.
Next, we define the key functions that will compute relative permeability and capillary pressure curves; Water-Oil, Gas-Oil, and Gas-Water systems.
def calculate_properties_water_oil(Sw, Swc, Sorw, Kro_max, Krw_max, no, nw, pc_max, npc):
kro = Kro_max * np.maximum(((1 - Sw - Sorw) / (1 - Swc - Sorw)), 0) ** no
krw = Krw_max * np.maximum(((Sw - Swc) / (1 - Swc - Sorw)), 0) ** nw
pcwo = pc_max * np.maximum(((1 - Sw - Sorw) / (1 - Swc - Sorw)), 0) ** npc
return kro, krw, pcwo
We also need code the functions to plot the curves in the user interface and export the curves as tables for ECL simulation.
The Streamlit library allows us to create an intuitive interface where users can select the system type, adjust parameters, and view results dynamically.
Key Elements:
At the end we can add a block for attribution
Sample attribution script:
st.markdown(
"""
---
Developed by [Shubham B. Patel](<https://www.linkedin.com/in/shubham-patel-1045/>) under the guidance of [Alan Mourgues](<https://www.linkedin.com/in/alan-mourgues/>).
Visit [CrowdField.net](<https://www.crowdfield.net/>) for more case studies.
"""
)
streamlit run Saturation functions app.py
my-streamlit-app
Clone the repository to your local machine:
git clone <https://github.com/your-username/my-streamlit-app.git>
Move your app files (e.g., app.py) into the cloned folder
Add a .gitignore file to exclude unnecessary files:
echo "__pycache__/" > .gitignore
Commit and push your code:
git add .
git commit -m "Initial commit"
git push
Create a file named requirements.txt in the repository folder.
Add the Python libraries your app needs:
streamlit>=1.42.0 # Streamlit for web applications
numpy>=1.21.0 # NumPy for numerical operations
pandas>=1.4.0 # Pandas for data manipulation
matplotlib>=3.5.0 # Matplotlib for plotting
scikit-learn>=1.1.0 # Scikit-learn for machine learning
requests>=2.27.0 # Requests for HTTP requests
plotly>=6.0.0 # Plotly for interactive plotting
PyPDF2>=3.0.0
xlsxwriter>=3.2.0
Save and push this file to GitHub:
git add requirements.txt
git commit -m "Add requirements file"
git push
main (or select your branch if it's different)app.py)requirements.txt and deploy your app
Once deployed, your app will be live, and you'll get a URL like:
https://your-username-your-repo-name.streamlit.app
Share this link with others to allow them to view your app.
Make changes to your app code locally.
Push changes to GitHub:
git add .
git commit -m "Update app"
git push
Streamlit Cloud will automatically detect changes and redeploy your app.
requirements.txt
That's it! Your app is now live on Streamlit and linked with GitHub for easy updates.
This project taught me several valuable lessons:
This Streamlit app offers a powerful yet user-friendly tool for calculating and visualizing relative permeability and capillary pressure curves. By combining technical rigor with modern visualization, it enhances understanding and accelerates decision-making in reservoir engineering.
These are the resources I used while developing this body of work:
You get:
Purchase for only $20:
👉 https://subscribepage.io/qKBwNA
I hope this article has shown that creating useful tools is within your reach—even if you're just starting with Python.
Feel free to reach out if you have any questions. Happy coding!
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Shubham is a Reservoir Engineer who specializes in deep-water and unconventional gas fields, focusing on reserve estimation, production forecasting, and reservoir modeling.
With expertise in well test interpretation, reservoir characterization, and economic analysis, he integrates analytical and numerical methods to optimize production and maximize asset value.
Committed to efficiency and sustainability, he enhances operations while promoting responsible reservoir management.
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