Data Analyst Engineer

SEYMUR MAMMADOV

Houston, TX 832-***-****

******@*****.*** linkedin.com/in/seymur-mammadov-190218b

DATA SCIENTIST

Dedicated and driven Data Scientist with strong engineering background in oil and gas. Adept at collecting, analyzing, and interpreting large datasets, developing new models, and performing data management tasks. Possess an extensive analytical expertise, strong attention to detail, and a significant ability to work in team environments.

TECHNICAL SKILLS

Languages: Python, R, SQL, JavaScript, VBA, MatLab.

Machine Learning: Scikit-Learn, Keras, Tensorflow, Pytorch, Computer Vision, Time Series, NLP, Classification, Regression, Unsupervised Learning, Deep Learning.

Data processing, visualization, ETL: numpy, scipy, pandas, matplotlib, seaborn, plotly, statsmodels, opencv, scikit-image, etc.

Cloud and Big Data: MS Azure ML, Virtual Machines, Apache Spark, Hadoop, Hive, NoSQL, Docker, and Kubernetes.

MLOps / DevOps: Agile (Kanban / Scrum), Azure DevOps, Git (Gitflow Workflow), MLflow, Unit Testing.

Business intelligence apps: Power BI, PowerApps, Spotfire and Tableau.

EXPERIENCE

BP Houston, TX 2004 – 2021

Data Scientist (October 2018 – January 2021)

Worked with multi-disciplined teams to solve data science problems in engineering using machine learning and deep learning. Explored Cloud and Big Data Technologies including MS Azure ML, Virtual Machines, Apache Spark, Hadoop, Hive, NoSQL, Docker, and Kubernetes. Led the projects with agile methodologies Kanban and Scrum using Microsoft Azure DevOps. Used Git for version control.

Analysed application of time series to understand the causes of production slugging on the GoM Platform. Built relationships between the events happening before the process upset to find the root cause of production slugging. Explored feature selection, feature importance methods.

Deployed the Boston Dynamics robot 'spot' to the GoM platform for a computer vision project. Used OpenCV, scikit-image for image processing, Mask R-CNN, YOLO v3, RetinaNet for Object Detection and Segmentation. The code was able to find dial gauges from taken pictures and read their values.

Led the Data Science project on Pipeline Wax Deposition with pig runs in the North Sea. This optimized the routine pigging operations to reduce Production Deferrals and OPEX. Combined time series data from datalogger, platforms and map distance data of the pipelines. Project results gave new insights to pipeline engineers and changed some of the previous beliefs on pig run behaviour.

Led the computer vision project on Shale Shaker Cutting Image Recognition. Worked with real-time video on the rig to determine the quantity and quality of cuttings coming from shale shakers. Used video processing techniques like, video stabilization, motion detection, and perspective transforms. Used deep learning for regression analysis. Applied transfer learning in Pytorch using ResNet-50 for image classification.

Data Analyst (January 2016 – September 2018)

Working in the Central Performance Team to provide drilling and completion benchmarking data analysis for the management in support of delivering safe, reliable, and competitive wells.

Single point of contact for all Global Wells central benchmarking activities on drilling, completions, and interventions. Provided technical support on benchmarking for all projects and processes within the Functional Performance Area.

Representing BP in external benchmarking activities including IHS Rushmore conferences and partner company meetings. Main contact for managing relationships between BP regions and IHS Rushmore company.

Developed performance metrics and procedure for benchmarking of completions time/cost and reliability/efficiency across BP regions. Developed BP Guide document for benchmarking wells performance consistently across all the regions.

Developed automatic processing of large OpenWells data using SQL and R languages.

Developed Central Wells Performance dashboards in Tableau, Spotfire and Power BI using JavaScript, SQL, R and Python languages over various databases in BP servers. These dashboards helped simplify and automate performance reporting process across the regions.

Conducted competitive wells benchmarking analysis on routine basis to identify gaps and/or areas of competitive advantages. These analyses provided operational and technical insights into global benchmarking reports and other outputs to help inform Global Wells Leadership Team decisions.

Tubular Design Specialist Sunbury, England (October 2012 – December 2015)

Casing and Tubing Design calculations and review work for BP projects in Eastern Hemisphere for all types of wells, including onshore, offshore, subsea, deep-water, HPHT.

Advanced use (super user) of WellCat and StressCheck for casing stress analysis, tubing stress analysis, wellhead movement calculations, packer loading envelopes, hanger lockdown analysis, buckling analysis, Worst Case Discharge and Well Capping analysis.

APB calculations in WellCat and analysing mitigation methods (VIT, Syntactic Foam, Burst Disc, etc.).

Probabilistic Casing Design calculations using limit state equations in MS Excel VBA and Crystal Ball software.

Performed casing wear analysis and wear calibration using Cwear software.

Software development in MatLab as an alternative to WellCat and StressCheck.

Developed automation of WellCat software for sensitivity analysis.

Prepared guides on how to apply BP casing and tubing design policies.

Mentored and helped region engineers on casing and tubing design problems.

Participated in peer review of regions’ well designs.

ADDITIONAL ROLES AT BP

Drilling Engineer Baku, Azerbaijan

PROFESSIONAL TRAINING

Preparing to Lead

Project Management

Decision Making and Cost Forecasting

Process Safety Fundamentals

EDUCATION

MSc Petroleum Engineering Heriot-Watt University, United Kingdom

BSc Applied Mathematics Baku State University, Azerbaijan

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