RMS Indicator Facies Modelling Data Sheet 2014

Indicator Facies Modelling
Reservoir management of mature giant fields requires the use of
modelling tools which can handle a large number of wells and which
can rapidly build and update the reservoir model. For giant fields,
RMSIndicators lets you rapidly build and continuously maintain 3D
facies models conditioned to thousands of wells and 3D seismic data.
It provides a fast and reliable foundation for reservoir management
and decision making.
• Fast and accurate pixel-based modelling.
• Handles very large numbers of wells quickly and efficiently.
• More complex models can be produced very easily by
integrating a variety of 1D, 2D and 3D geological trends.
Simulations run so fast that you can easily fine-tune the parameters by
hand, either trend selection or variogram values, to get optimal results.
• The modelling process is straight forward, with an intuitive
• Seamless links to volumetric calculations, connectivity
analysis, full well planning functionality and reservoir
simulation carry you smoothly through your workflow.
• Conditions to seismic data using a unique indicator
co-simulation algorithm.
• Incorporates large numbers of wells quickly and efficiently.
• Uses the tried and tested Sequential Indicator Simulator
Fast, Simple Facies Modelling
RMSIndicators is a simple and quick tool for generating facies models. It
can run with a minimum of user data, or it can use additional geological
information. It can accommodate unlimited amounts of well data. It is
straightforward to set up and can use seismic or trends to refine the
Indicator modelling is a ‘data driven’ technique, i.e. the parameter values
are derived from input data. The standard input to the indicator
modelling includes the well data, volume fractions and indicator
variograms. Variograms give an indication of the variability of the facies
in a given direction. Changing the length of the variogram axis controls
facies continuity in that direction as seen on the right of the 2 by 2 figure.
A variety of trends can be used as additional geological input to the
modelling. Facies information is taken from the blocked wells and each
facies type can follow its own trend. Total facies volume is constrained to
equal 1 whilst matching the individual volume fractions.
Seismic data can also be used to constrain the facies distributions. The
seismic can either be used as a directly correlated seismic to facies value
or as a more subtle probabilistic relationship between seismic attribute
and facies type.
Indicator Facies Modelling
Integrating Geological Trends
Modelling Giant Fields
RMSIndicators can model any number of facies and can use 1D, 2D
and 3D geological trends to constrain the facies distribution.
RMSIndicators is the true “millions of cells, thousands of wells”
technology for facies modelling. The figure opposite shows an
indicator model generated for a mature giant oil field with 25 million
cells and 1000 wells.
For example, vertical geological trends are
defined using vertical proportion curves, such as
illustrated left showing proportion curves for
two coal-capped upwards-coarsening cycles.
These proportion curves have been used as
input for the facies model shown in the
cross-section below. Note the continuity of the
two coal-rich horizons
Other typical geological trends include maps
that are used to constrain the lateral facies
distribution, for example, a trend of increasing
water depth can control increasing shale
fraction or constrain patch reef development.
Simulation takes minutes
not hours on a standard
To learn more please visit www.roxarsoftware.com or email
us on rss.marketing@emerson.com.
Seismic Constraints
RMSIndicators provides two methods for using seismic data for
modelling facies distribution.
• Conditional probabilities
• Indicator co-simulation
The conditional probabilities method provides similar results to using
a 3D trend.
Roxar 2014
The indicator co-simulation method is unique to this module. It is
analogous to conventional co-kriging methods, but is applied to
facies indicators. This method provides an objective correlation
co-efficient between the seismic and faces data.