PetroTech Well Production Simulator & Decline Curve Analysis

Context

In petroleum and reservoir engineering, estimating future hydrocarbon recovery and forecasting production rates over time is vital for asset valuation, field development planning, and economic feasibility assessments.

Challenge

Modeling exponential production rate declines across multiple wells with varying initial rates, decline coefficients, and operational lifespans without manual spreadsheet errors.

Key Questions

• How does nominal exponential decline rate sensitivity affect estimated ultimate recovery over a multi-year horizon?
• How can engineers quickly configure and compare production profiles across diverse field wells?
• What computational framework best blends deterministic physics formulas with interactive data visualization?

Approach

• Implemented J.J. Arps' empirical exponential decline model into a flexible Python simulation engine.
• Designed a multi-well parameter registration engine supporting custom initial rates, decline rates, and forecast lifespans.
• Calculated vital reservoir analytics including cumulative production and average annual production rates.
• Generated automated visual production decline curves and formatted tabular summaries for engineering reports.

Outcomes

• Built a robust, deterministic simulation notebook and Python tool for rapid well performance forecasting.
• Enabled instant multi-well parameter comparison and decline trajectory forecasting for petroleum engineering workflows.
• Delivered as the Capstone Project for the prestigious PetroTech 2.0 Program.

Takeaways

• Empirical analytical models remain foundational in reservoir engineering for rapid, interpretable asset screening.
• Clean computational code replaces brittle spreadsheets while ensuring reproducible decline curve calculations.
• Combining mathematical modeling with interactive graphical outputs accelerates engineering decision-making.