{"id":28627426,"url":"https://github.com/ser-arthur/volve-field-data-analysis","last_synced_at":"2026-02-15T08:33:07.536Z","repository":{"id":286836740,"uuid":"948514378","full_name":"ser-arthur/volve-field-data-analysis","owner":"ser-arthur","description":"data science project for oil well production data from the North Sea Volve Field. 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The dataset consists of daily production data for multiple wells, and the\nanalysis is conducted using Python, Pandas, Matplotlib, and Seaborn.\n\n## Objective\nThe goal of this analysis is to:\n- Examine cumulative oil, gas, and water production trends. \n- Investigate well-level production performance. \n- Analyze the impact of water injection on production. \n- Identify signs of water breakthrough and production decline patterns. \n- Study Gas-Oil Ratio (GOR) trends to understand reservoir depletion. \n- Evaluate Water-Oil Ratio (WOR) trends to assess water breakthrough timing. \n- Explore pressure behavior in relation to production decline.\n\nYou can preview project in jupyter notebook here [Volve-Field Analysis](https://nbviewer.org/github/ser-arthur/volve-field-data-analysis/blob/main/volve-field-data-analysis.ipynb)\n\n## Dataset\n\nThe dataset includes:\n - Daily production data for wells in the Volve Field.\n - Key parameters such as oil, gas, and water production rates, and pressure data.\n - Injection data for water injector wells.\n - GOR and WOR data for analyzing fluid behavior. \n - Pressure measurements to study reservoir depletion.\n\n## Key Analyses \u0026 Insights\n**Field-Level Production Analysis:**\n- Evaluates total oil, gas, and water production trends.\n- Identifies key production phases (ramp-up, peak, decline).\n\n**Well-Level Production Performance:**\n- Compares oil, gas, and water output across different wells.\n- Highlights high-performing and low-performing wells.\n\n**Gas-Oil Ratio (GOR) \u0026 Water-Oil Ratio (WOR) Trends:**\n- Tracks GOR changes over time to assess reservoir depletion. \n- Analyzes WOR trends to detect early signs of water breakthrough.\n\n**Water Injection \u0026 Production Correlation:**\n- Examines how water injection influenced production rates. \n- Identifies wells affected by water breakthrough.\n\n**Pressure vs. Production Analysis:**\n- Studies how downhole pressure trends correlate with oil and gas production decline.\n- Evaluates pressure support from injection wells and its effect on production.\n\n**Operational Time \u0026 Production Efficiency:**\n- Assesses well uptime and how it relates to production performance.\n\n## Technologies Used\n - Python\n - Pandas (for data manipulation)\n - Matplotlib \u0026 Seaborn (for data visualization)\n - Jupyter Notebook (for interactive analysis)\n\n## How to Use This Notebook\n - Clone this repository or download the notebook.\n - Open the Jupyter Notebook and run the cells sequentially.\n - Ensure you have the necessary dependencies installed: `pip install pandas matplotlib seaborn` \n - Load the dataset and explore the visualizations and insights.\n\n## Future Work\n- Expanding pressure analysis to explore pressure drawdown effects on well performance.\n- Implementing production forecasting \u0026 decline curve analysis.\n- Studying GOR behavior at different production stages for better reservoir management.\n- Enhancing injection-efficiency analysis to improve secondary recovery insights.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fser-arthur%2Fvolve-field-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fser-arthur%2Fvolve-field-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fser-arthur%2Fvolve-field-data-analysis/lists"}