Course Curriculum

Install Python, configure virtual environments, and set up Jupyter for engineering workflows.

Variables, functions, control flow, and OOP — tailored for petroleum engineers.

Lists, dicts, sets, and tuples for organizing well data and production records.

Vectorized computation with NumPy arrays and tabular analysis with Pandas DataFrames.

Matplotlib, Seaborn, and Plotly for well logs, cross-plots, and production charts.

Import LAS files, clean production CSVs, and handle missing data in field datasets.

Compute porosity, water saturation, and net pay from triple-combo logs.

Standing, Vasquez-Beggs, and Lee-Gonzalez correlations for oil, gas, and water properties.

Arps hyperbolic, exponential, and harmonic models for production forecasting.

Havlena-Odeh graphical method and generalized material balance for OOIP estimation.

Finite-difference grids, IMPES method, and history matching with Python.

Inflow performance, vertical lift performance, and system analysis for well optimization.

Gas lift, ESP sizing, and rate allocation across multi-well pad systems.

Predict rate of penetration and detect drilling dysfunctions from sensor data.

Z-factor correlations, gas compressibility, and deliverability calculations.

Scikit-learn basics — train/test splits, cross-validation, and feature engineering.

Regression and classification models for facies prediction and production estimation.

K-means, PCA, and DBSCAN for rock typing and anomaly detection.

Neural networks, CNNs for seismic, and LSTMs for time-series production data.

Prompt engineering, RAG pipelines, and fine-tuning LLMs for petroleum Q&A.

End-to-end projects combining data engineering, modeling, and visualization.

Build interactive Streamlit and Dash apps for production monitoring dashboards.

Docker, AWS, and CI/CD pipelines for deploying petroleum analytics at scale.

Career paths in energy data science, portfolio building, and interview preparation.