Technical Glossary

Application Programming Interface — a set of protocols for building software. In Python, libraries like lasio and pandas expose APIs for data manipulation.

A natural reservoir drive mechanism where water from an adjacent aquifer encroaches into the hydrocarbon zone, maintaining pressure. Modeled in Python using carter_tracy or fetkovich functions.

The pressure at which the first bubble of gas evolves from an oil sample at reservoir temperature. Calculated via PVT correlations (Standing, Vasquez-Beggs) in Python.

A NumPy mechanism that allows arithmetic operations on arrays of different shapes. Essential for vectorized petroleum calculations across depth intervals.

Fundamental equation governing fluid flow through porous media: q = -(kA/mu)(dP/dL). Implemented in Python using scipy.sparse.linalg for reservoir simulation grids.

The primary two-dimensional data structure in Pandas. Used extensively for well logs, production histories, and PVT tables throughout this book.

A type-curve method for decline curve analysis that combines transient and boundary-dominated flow. The fetkovich module provides automated curve matching in Python.

A Python string literal prefixed with 'f' that supports inline expressions: f'Rate: {q:.1f} STB/d'. Used throughout for formatted engineering output.

Gas-Oil Ratio — the ratio of gas produced to oil produced, typically in SCF/STB. A key PVT parameter tracked in production DataFrames.

Pressure exerted by a fluid column: P = 0.052 * rho * TVD. One of the first calculations automated in Python in Chapter 1.

Inflow Performance Relationship — describes the relationship between bottomhole flowing pressure and production rate. Modeled using Vogel's or Fetkovich's equation in Python.

In machine learning, a function that computes similarity between data points in a higher-dimensional space. Used in SVMs for lithofacies classification.

Log ASCII Standard — the industry-standard format for well log data. Read in Python using the lasio library: lasio.read('well.las').

Fundamental reservoir engineering equation relating cumulative production to reservoir pressure decline. Implemented as the Havlena-Odeh graphical method in Chapter 10.

The fundamental package for numerical computing in Python. Provides ndarray objects and vectorized operations critical for engineering calculations.

Original Oil In Place — the total volume of oil in a reservoir before production. Estimated via volumetrics or material balance in Python.

A measure of a rock's ability to transmit fluids, measured in millidarcies (mD). Estimated from well logs using empirical correlations in Python.

A powerful data analysis library for Python. Its DataFrame and Series objects are used on every page of this book for engineering data manipulation.

Rate of Penetration — the speed at which a drill bit advances, typically in ft/hr. Predicted using machine learning models in Chapter 14.

A dimensionless parameter representing near-wellbore damage or stimulation. Positive = damage, negative = stimulated. Used in well test analysis.

Scientific computing library for Python. Provides optimization (curve_fit), integration (trapezoid), and linear algebra tools used in reservoir engineering.

A pre-computed dimensionless solution for well performance analysis. Fetkovich and Blasingame type curves are implemented in Python for DCA.

An empirical IPR correlation for solution-gas-drive reservoirs: qo/qmax = 1 - 0.2(Pwf/Pr) - 0.8(Pwf/Pr)^2. Implemented in nodal analysis (Chapter 12).

Fraction of pore space occupied by water (Sw). Calculated from resistivity logs using Archie's equation: Sw = ((a * Rw) / (phi^m * Rt))^(1/n).