Ildar A.

Applied Mathematician in physics-based and data-driven modelling of oil well production. Open for freelance / consulting / collaboration.

Email: abdulin.ildar.ai@gmail.com
Location/Work Permit: Netherlands
LinkedIn: linkedin.com/in/abdulin/

📝 Publications

Google Scholar: https://scholar.google.com/citations?user=jbHl0wQAAAAJ
1. Oil Industry Journal (Peer-reviewed, Scopus-indexed), Production forecast method based on statistical analysis of a small sample of production data for an unconventional formation, 2021.
2. Oil Industry Journal (Peer-reviewed, Scopus-indexed), Identification of fractal properties and parameters upscaling of layered heterogeneous medium, 2020.
3. American Institute of Physics Conference Series (Peer-reviewed, Scopus-indexed), Numerical investigation of radial steady-state fluid flow model with Riesz potential, [PDF], 2020.
4. One-Dimensional Models of Single-Phase Fluid Flow in Media with Fractal Structure, Conference "12th All-Russian Congress on the Fundamental Problems of Theoretical and Applied Mechanics", 2019.
5. Modified Buckley-Leverett Models Based on Power Law and Fractal Calculus, Conference "Nigmatullin Readings" (Math Modeling), 2018.
6. Mathematical Models of Fluid Flow in Porous Media with Fractal Properties of Geometrical Characteristics, Conference "Current Issues in Applied Mathematics", 2018.
7. (page 299) Models of Two-phase Filtration in Fractal Porous Media, Conference "International Conference on Mathematical Modelling in Applied Sciences", 2017.

🎓 Education and Certifications

MSc of Applied Mathematics and Computer Science, Ufa State Aviation Technical University (USATU), Russia.
BSc of Applied Mathematics and Computer Science, USATU, Russia.
Extra Training, Analysis and Math Modeling of Oil Production, Rosneft, USATU, 180 hours.
2021, Online Course, Machine Learning (Program), Andrew Ng, Coursera, [Coursera Certificate], 60 hours.
2020, Online Course, Data Analysis Based on Machine Learning, BashSU, Ufa, Russia, 72 hours.
2024, HackerRank, Python Test Certificate.

📈 Experience

Gazprom Neft Oil Company | Remote – Mathematical Modeling Engineer
03/2020 – 09/2020, 08/2021 – 01/2022, 08/2022 – 01/2023 (1.5 years total)
Worked on 3 projects focused on analysis and development of predictive modeling modules for oil production.
Key Responsibilities:
• Research on predictive modeling techniques in oil productions.
• Designed and implemented data mining and modeling solutions using Python 3 and VBA.
• Development of technical requirement and documentation.
Publications: [1].
About Company: The Technology Partnerships LLC (Bazhen Technology Centre) department, based in Moscow, specializes in shale oil production modeling. About Department, Gazpromneft Wiki Page.

GAMMETT Research Laboratory, USATU | Hybrid – Research Engineer
03/2017 – 11/2020 (3 years 8 months)
Engaged in validating academic mathematical models for industrial applications, including prototype development and problem detection in oil production modeling.
Key Responsibilities:
• Research on modeling techniques to account for new phenomena in oil production.
• Designed computational solutions using Maple and Python 3 for advanced modeling tasks.
• Published academic papers in mathematical modeling and prepared detailed research reports.
Publications: [2, 3, etc.].
About Company: The research lab GAMMETT, based at Ufa State Aviation Technical University (Russia) collaborated with the research institute RN-BashNIPIneft on oil production modeling research. Website.

💼 Projects

1. Gazprom Neft | Hydrodynamic Oil Production Forecasting (2D Quasilinear Diffusion Equation) | Python 3 | 08/2022 – 01/2023
• Objective: Develop open-source alternative to commercial simulators for production forecasting under reservoir engineering constraints
• Implementation: Designed finite element solver (FiPy) implementing pressure-dependent permeability modeling, benchmarked against tNavigator
• Outcome: Achieved 95-99% parity with industry-standard simulator results while eliminating arbitrary parameter tuning [GitHub: Solver Implementation]

2. Freelance | Commercial Real Estate Valuation Analytics (Moscow Market) | Python 3 | 05/2023 - 07/2023
• Objective: Identify systematically undervalued commercial properties in high-potential Moscow districts
• Implementation: Built CIAN.ru web scraper (BeautifulSoup/CloudScraper) + XGBoost valuation model with expert-defined feature engineering
• Outcome: Delivered 3 expert-validated investment opportunities within MVP period, reducing noise by 40% through NLP filtering [GitHub: Parser Architecture]

3. Gazprom Neft | Excel-to-VBA Forecasting Model Migration | VBA | 08/2021 – 01/2022
• Objective: Modernize legacy spreadsheet-based forecasting model (600+ formulas across 7 sheets)
• Implementation: Developed automated formula parser converting Excel logic to modular VBA with unified GUI interface
• Outcome: Eliminated manual calculation errors and improved operational efficiency through single-interface solution [GitHub: Conversion Module]

4. Gazprom Neft | Production Drivers Analysis & Stochastic Forecasting | Python 3 | 03/2020 – 09/2020 | [1] – Publication
• Objective: Quantify key production influencers and develop probabilistic forecasting framework
• Implementation: Multivariate regression analysis + Monte Carlo simulation
• Outcome: Identified 5 statistically significant production drivers with SHAP value interpretation

5. Research lab. GAMMETT | Fractal Reservoir Pressure Modeling | Maple | 2020 | [2, 4] – Publications
• Objective: Incorporate fractal properties of the medium into a mathematical model for oil pressure.
• Implementation: Designed a new model in collaboration with a Physicist Scientist. Tested using real-world data.
• Outcome: The use of the model can improve the accuracy of predicted values by up to 20%.

6. Research lab. GAMMETT | Singular PDE Numerical Methods | Maple/Python 3 | 2019 | [3] – Publication
• Objective: Develop stable numerical scheme for integro-differential equation
• Implementation: Created adaptive mesh refinement algorithm handling solution singularities
• Outcome: Published convergence proof and field validation results

7. Research lab. GAMMETT | Fractal Derivatives in Oil Production Modeling | Maple / Python 3 | 2018 | [5, 6] – Publications
• Objective: Develop and compare new modeling approach (fractal derivative) for physical model with differential equations (oil production)
• Implementation: Prototyped a Monte Carlo algorithm article arxiv.org/abs/0906.0676. Developed a model with fractal derivatives in collaboration with two Research Scientists.
• Outcome: Conducted comparative analysis with a simpler model. [EN Description]

🛠️ Skills

Professional Experience: Data Minig, Data Visualization,
Statistical Analysis/ML, Math Model Prototyping.
Scripting: Python 3, Maple/Matlab, SQL, VBA.
Languages: English - B2, Russian - Native.