Petrophysical Modelling

An accurate description of petrophysical properties, such as porosity and permeability, and their spatial variability is essential to understanding the fl uid distribution and fl ow properties of a reservoir. RMSTM offers a full suite of petrophysical modelling tools from 2D interpolation to 3D stochastic modelling conditioned to wells, facies and seismic data. Models can be updated globally or locally as new data is acquired, keeping the models accurate and the decisions focused.

Fast, Simple Petrophysical Modelling

Petrophysical modelling should always be based on combining all available data, especially well and seismic, with the geological interpretation of the reservoir. To achieve this, RMS provides fully integrated data analysis tools and a toolbox of easy-to-use geological trends. These trends are used to incorporate knowledge of the depositional environments and diagenetic processes directly in the petrophysical model. The petrophysical modelling tools in RMS include standard distance weighting methods, a full suite of kriging methods and stochastic simulation based on a Fast Fourier Transform (FFT) algorithm which ensures accuracy and high speed performance. The kriging methods include simple, ordinary, universal, Bayesian and co-located co-kriging. The kriging algorithms have been optimised to handle giant fi elds with thousands of wells.

Integrating Geological Trends

The petrophysical properties of a reservoir are strongly controlled by depositional facies and a variety of post-depositional diagenetic processes. RMS has an extensive and easy-to-use trend toolbox that can be used to incorporate a wide variety of trends in the petrophysical model including:

• large-scale compactional/depositional processes• intrabody trends - upwards fi ning or coarsening - proximal-to-distal - axis-to-margin• lateral trends• facies related• skewed distributions including normal score• cloud transform

IN SUMMARY

• Complex models can be produced very easily by integrating a variety of geological trends.

• The model can be conditioned to seismic data.

• Wide range of data transformations allow you to select the best for your data.

• Full suite of interpolation, kriging and simulation algorithm.

• Seamless links to volumetric calculations, connectivity analysis, full well planning functionality and reservoir simulation carry you smoothly through the workfl ow.

• Full model updating allows you to keep your model continuously correct.

• Handles very large numbers of wells quickly and effi ciently.

ROXAR AS, GAMLE FORUSVEI 17, PO BOX 112, 4065 STAVANGER, NORWAY TELEPHONE +47 51 81 8800 FAX +47 51 81 8801 WWW.ROXAR.COM

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Petrophysical Modelling

Modelling Giant Fields

The 3D petrophysical modelling tools in RMS include unique, fast and memory effi cient kriging and simulation algorithms that allow you to model giant reservoirs with millions of cells and thousands of wells. The fi gure below illustrates a 3D petrophysical model generated for a mature giant oilfi eld with 25 million cells and 1000 wells. This model takes only a few minutes to run.

Petrophysics at Your Fingertips

The RMS portfolio includes the benchmark industry tools for petrophysical modelling. These tools allow you to integrate all available data when building petrophysical models of even the largest and most complex reservoirs. The seamless integration of the petrophysical modelling tools in RMS allows direct access to volumetric calculations, upscaling tools, well planning tools with mechanical constraints and a fully integrated fl ow simulator. It also allows you to make full use of the workfl ow management facilities to automate the model building and updating process, to test multiple scenarios and quantify uncertainty.

Making Use of Seismic Data

With the increasing availability and quality of seismic data, it is paramount to incorporate this data in the reservoir model. RMS provides a selection of methods which allows seismic data to be fully utilised in petrophysical modelling, including the SedSeis module, which allows you to blend the seismic data and interpretations with the stochastic modelling techniques to close the resolution gap between the two methods. The fi gure below illustrates a porosity model which has been generated using a co-simulation technique with well data and a seismic acoustic impedance cube as input.

To learn more please visit www.roxarsoftware.com or emailus on rss.marketing@emerson.com.