Well Test Analysis with Regression

This book provides a comprehensive understanding of the fundamental theories of well-testing analysis and its application through analytical diagnostics, regression, and computational examples. Its main features are: 1. Connecting Physical Understanding with Numerical Regression: The book develops governing equations, analytical solutions, and flow-regime diagnostics, showing how classical interpretation methods guide model selection and numerical regression. Readers learn to estimate reservoir and well parameters, recognize nonunique interpretations, and evaluate the reliability of fitted results. 2. Practical Python Integration: The book introduces developed Python code for well- testing analysis, guiding readers through its application in real-world scenarios. This hands-on approach demystifies the use of programming and existing software in well test analysis. 3. Bridging Analytical Theory and Computational Practice: Detailed worked examples and accompanying Python tools connect mathematical formulations with practical calculations. Readers can explore model behavior, examine parameter sensitivity, compare classical estimates with numerical fits, and adapt the computational methods to their own engineering problems. The examples emphasize data quality, interpretation assumptions, and critical assessment of results. 4. Integrating Well Testing, Surveillance, and Production Forecasting: The book connects pressure-transient analysis with rate-transient analysis through superposition, convolution, deconvolution, and pressure–rate duality. Applications span conventional well tests, injection wells, permanent downhole measurements, fractured reservoirs, and advanced completions, providing a consistent framework for interpreting both controlled tests and routine production histories. By the end of the book, readers will be better equipped to select appropriate models, interpret pressure and rate data, assess uncertainty, and construct defensible production forecasts. The book serves graduate students, practicing engineers, and researchers seeking both a rigorous foundation and practical computational methods for dynamic reservoir characterization.