Software Development
Automated Pipeline Network Modeling Software
01. Brief
Led the development of a software application 🏭 using Python 🐍 and VBA to enable scalable pipeline network modeling for Chevron, a world-leading Oil & Gas company, ✅cutting investment costs by $49.8 million.
02. Skills
Software Development, Python, Excel Macro VBA, Data Structure, Database, Search Algorithms
03. Achievements
$49.8 million Less Investment
Researched for multiple pipeline network scenarios through the software to determine the most optimal solution, resulting in a significant reduction in investment costs.
Less Workload
Streamlined pipeline network modeling, creating a more efficient and scalable research framework. The new framework offers optimized investment opportunities and has drastically reduced team workload for the research team.
Scalable Research
Successfully identified the most optimal pipeline network scenario through scalable research framework, resulting in operational efficiency, reduced maintenance requirements, and significant investment cost savings
04. Problems
At Chevron, we use specialized petroleum simulation software to model complex pipeline systems in the oil and gas industry. This software is crucial for providing a detailed understanding of the behavior of fluids and gases within pipelines, allowing for accurate predictions and simulations of various pipeline scenarios.
However, we encountered challenges when transferring big amounts of pipeline network information stored in an Excel file to the specialized petroleum simulation software. The transfer process of this data was manual and, hence, it was inefficient and prone to errors, resulting in outdated information that was not suitable for research with various pipeline conditions. This also hindered our ability to optimize pipeline installation design.
05. Actions
• Developed an in-house application that automates the process of data transferring between Excel and the specialized petroleum simulation software.
• The connection between Excel and the software requires a specific technology that is not commonly available on the internet. I had to rely on documentation to learn this programming language from scratch
• Utilized a scalable research framework to explore a vast number of pipeline network scenarios, identifying the most optimal one through the use of Python for visualization and analysis of large amounts of data.