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Web Application for Monitoring Oil Well Sensor Data
Client: Oilfield Service Company
Backend
Expertise:
Tech Stack:
Frontend
Python
Angular
DevOps
Azure DevOps
Kubernetes
K3s
The client is a major company specializing in developing solutions for monitoring and managing oil wells. The company aimed to expand the capabilities of its scientific research by transforming it into a commercially successful product, offering clients digital tools to monitor data from oil well sensors.
About the Client:
The project involved creating a web application to handle data from sensors installed on oil wells. The goal was to integrate the client's existing scientific research into a marketable solution that could be sold either as a standalone product or as a service. The main focus was on providing a user-friendly interface for real-time data monitoring while ensuring the system's reliability and scalability.
About the Project:
The task was to develop a web application with extended functionality, including:
  • Real-time sensor data monitoring: The web application needed to process sensor data in real-time, allowing users to track key metrics and respond to changes as they occur.
  • Data analysis and visualization: The application should provide tools for interpreting the incoming data, offering clear visualizations of key parameters.
  • Scalability: The application needed to support multiple users and function effectively both as a cloud service and an on-premise solution.
  • Integration with existing systems: Ensuring seamless integration with the client’s current oil well management systems and supporting future scalability.
Task Description:
01
Backend
  • Python for data processing and handling complex computations related to sensor monitoring.
02
Frontend
  • Angular for building an intuitive user interface that allowed operators and analysts to easily access and manage real-time data.
Implementation:
The following technologies were used to execute the project:
03
CI/CD and Containerization
  • Azure DevOps and Kubernetes were used for deployment and managing the cloud service, ensuring high reliability and scalability.
04
On-premise Solution
  • For clients requiring standalone solutions, a Kubernetes-based system, k3s, was utilized to deploy the application on local servers.
01
Research and Design
Analyzing the client’s existing scientific research and adapting it for commercial use.
02
Backend and Frontend Development:
Creating a reliable system for collecting sensor data and an easy-to-use interface for handling this data.
The key stages of development included:
03
Integration of Analytical Tools
Implementing tools for data visualization and analysis, so users could quickly interpret the results.
04
Ensuring Reliability and Security
Using Kubernetes and Azure DevOps to ensure stable operation and the ability to scale quickly.
05
Testing and Launch
The application underwent extensive testing to guarantee efficient performance in both cloud and on-premise environments.
The developed web application was successfully integrated into the client's workflow and is now sold as a commercial product to the company’s clients. Key outcomes include:
  • Creation of a commercially viable product based on the client’s scientific research.
  • Flexibility in offering the solution both as a cloud-based SaaS and as an on-premise product.
  • Increased performance and quality of oil well monitoring, thanks to enhanced data visualization and analytical capabilities.
  • The product became in-demand in the market, helping the client expand their offerings and attract new customers.

This solution allowed the client to bring their scientific research into a commercial context, increasing its appeal and functionality for end users.
Outcome:

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