Ildar Abdulin

Proficient in ML & Statistics, Math Models with PDEs, Data Visualization. Skilled in predictive engineering analytics and quantitative research.

Location: EU     LinkedIn: linkedin.com/in/abdulin/

πŸ“ Publications

1. Production forecast method based on statistical analysis of a small sample of production data for an unconventional formation, Oil Industry Journal (Scopus), 2021.
2. Identification of fractal properties and parameters upscaling of layered heterogeneous medium, Oil Industry Journal (Scopus), 2020.
3. Numerical investigation of radial steady-state fluid flow model with Riesz potential, [PDF], American Institute of Physics Conference Series (Scopus), 2019.
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.

πŸ’Ό Projects

1. Gazprom Neft | Oil Production Forecasting Model (2D Diffusion Equation) | Python 3 | 08/2022 – 01/2023
β€’ Goal: Develop a predictive mathematical model to forecast oil production volumes. Restriction: using open-sourse methods.
β€’ Solution: Designed a prototype using the 2D diffusion equation in Python 3, collaborating with two Reservoir Engineers.
β€’ Result: Achieved 95–99% accuracy compared to the commercial hydrodynamic simulator tNavigator. [Github Link Solver]


2. Freelance | Real Estate Underpricing Analysis | Python 3 | 05/2023 - 07/2023
β€’ Goal: Identify undervalued real estate properties in a specific market segment in Moscow.
β€’ Solution: Developed a real estate data parser and an outlier detection algorithm using a gradient boosting price model. Collaborated with two Real Estate Experts.
β€’ Result: Discovered three actionable insights within one month. [Github Link Parser]


3. Gazprom Neft | VBA-Based Oil Production Model Enhancement | VBA | 08/2021 – 01/2022
β€’ Goal: Improve usability and efficiency of the company’s oil production forecasting model.
β€’ Solution: Transferred a spreadsheet with over 600 formulas to a VBA-based prototype with a GUI and enhanced the mathematical framework.
β€’ Result: Automated complex calculations, significantly improving usability. [Github Link Spreadsheet to VBA Transfer]


4. Gazprom Neft | Oil Well Data Mining and Forecasting | Python 3 | 03/2020 – 09/2020
β€’ Goal: Identify key factors influencing oil well production and improve forecasting accuracy.
β€’ Solution: Developed a regression model for oil production volume forecasting. Collaborated with two Reservoir Engineers.
β€’ Result: Identified 5 critical factors influencing production. Conducted oil production volume forecasting using the Monte Carlo simulation. [1] – Publication of research results.


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


6. Research lab. GAMMETT | Numerical Algorithm for Singular Math Model | Maple / Python 3 | 2019
β€’ Goal: Conduct numerical experiments with a new mathematical model with singularities.
β€’ Solution: Created a numerical method for a mathematical model in collaboration with a Research Scientist.
β€’ Result: Developed a model prototype. Demonstrated the physical adequacy of the model's numerical approximation. [3] – Publication of research results.


7. Research lab. GAMMETT | Fractal Derivatives in Oil Production Modeling | Maple / Python 3 | 2018
β€’ Goal: Develop and compare new modeling approach for fractal in mathematical models of oil production.
β€’ Solution: Prototyped a Monte Carlo algorithm article arxiv.org/abs/0906.0676. Developed a model with fractal derivatives in collaboration with two Research Scientists.
β€’ Result: Conducted comparative analysis with a simpler model. [5, 6] – Publications of research results. [EN Description]

πŸ“ˆ Relevant Experience

πŸŽ“ Education and Certifications

πŸ› οΈ Skills

πŸ“‹ Additional Information

EDA Jupyter Notebook, Regression of Used Car Prices, Kaggle Competition, 09.2024.