Mahammad Ojagzada

About Me

I am a Researcher/Data Scientist/Engineer with a background in Petroleum Engineering, skilled in applying data-driven solutions to engineering problems and performing segmentation and statistical analyses on large datasets using tools like SQL, PySpark, Tableau and Python. I have worked on 4+ projects of BP at the Digital Research Lab. I like to create algorithms/pipelines for data solutions and I am passionate about leveraging data science in specialized fields.

Education

Reservoir Evaluation and Management, Master of Science
Baku Higher Oil School 2024 - Present, Baku

Petroleum Engineering, Bachelor of Science
Baku Higher Oil School 2019 - 2024, Baku

Developed technical knowledge on Petroleum Engineering and participated in internships with major national oil operating and service companies.
Benchmarked the analytical and numerical solutions of the Buckley-Leverett theory in 1D, demonstrating the accuracy and efficiency of numerical methods
in modeling fluid flow in porous media.

Work Experience

Data Engineer/Researcher

Digital Research Lab

Feb, 2023 - Present

Worked on various data engineering and research projects, including developing scalable data pipelines and creating dashboards for data analyses
Applied data-driven solutions to engineering problems and performed segmentation and statistical analyses on large datasets.
I worked on the following projects of BP at Digital Research Lab.

Projects

Reservoir Depletion Operating Limit

Engaged on heavy back-end data manipulation and created a comprehensive dashboard on Palantir Foundry about petroleum reservoir characteristics (H2S, pressure, etc.)

Tools: SQL, JavaScript, Python, Palantir Foundry

Techlog Automation

Created an advanced, scalable Python utility designed to enhance the creation and visualization of well logs. These Onepagers are steamed to SharePoint via Azure.

Tools: Advanced Matplotlib, Python, MS SQL, SQL Alchemy, Multi-processing

Sustained Casing Pressure Detection

Created a Python tool to detect annulus pressure build-up, TBA communication in any well annulus using analytical methods and SVM Classifier model.

Tools: Python, SQL, PySpark, Machine Learning, Palantir Foundry

Wax Precipitation Prediction

Created data pipelines that collect data, feed data into an MLP model, and store it incrementally over time in Palantir Foundry using Code Repositories and Model Adapter.

Tools: Python, PySpark, Machine Learning, SQL, Palantir Foundry