Petrel petrophysical modeling

Build accurate petrophysical models

Overview

Petrel™ petrophysical modeling enables the population of geocellular 3D grids with continuous data—whether the distribution is to be conditioned to well data, to previously modeled facies properties, or to both. The module provides the tools users need to easily and accurately model reservoir properties such as porosity, permeability, net to gross, and saturations.

Create realistic shared earth models incorporating all available data through an extensive list of petrophysical modeling algorithms.

Petrophysical data can be conditioned to facies models and visualized with well and seismic data in 3D and well sections.

Local model update, outside the area of interest the property remains the same, ensuring history matched data is not overwritten.

Increase confidence in volume estimation by generating porosity models interpolated from well data supported by seismic trends.

Complex and reverse faulted structures can be modeled with stair-stepped grids and further populated with facies and porosity values.

Geophysicists also benefit: well velocity data and stacking velocities can be combined to generate 3D velocity models.

A porosity model viewed depositional space.

Applications

Petrophysical models can be conditioned to facies models.
Custom algorithms can be created and run in combination with other available methods.
Trend and secondary data, such as logs, properties, vertical function, and seismic data, can be used with collocated methods.
Dynamic QA maps can be generated. Modeling input parameters can be saved as objects to reuse, share, and include in workflows.

Benefits

The extrapolation and modeling algorithms are complemented by extra functionality, such as property calculators, filtering options, and mathematical functions. The module also enables the scale-up of high-resolution well log data and point attribute data into the geocellular grid. With these modeling dialogs open concurrently, the effect of changes to scale-up parameters, data analysis settings, or property modeling algorithms are visualized instantaneously.

A porosity model viewed in geological space.

Features

Deterministic modeling algorithms include traditional kriging algorithms and moving average, which is based on inverse distance weighting.
Stochastic modeling algorithms include sequential Gaussian simulation and Gaussian random function simulation.
Local property model update is available to regenerate the values of cells only in the area to be updated while keeping consistency and continuity with the surrounding cells in the grid.
The modeling input parameters editor is enabled for petrophysical modeling, which supports editing, viewing, exchanging, and reporting all petrophysical modeling parameters inside a flexible, unified, tabular interface.

Training