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Multiple-point Statistics (MPS)Get realistic images of the internal structure of your reservoir

Isatis offers a powerful implementation of the

Multiple-point Statistics (MPS) algorithm which

integrates the optimized Impala1  v2.0 library by

Ephesia Consult. 

Multiple-point Statistics is a facies modeling technique

based on multiple-point statistics instead of the

conventional variogram-based techniques founded on

bi-point statistics. It offers another way to model

complex and heterogeneous geological environments

through the use of a training image which describes

the geometrical characteristics of the facies to model.

The method allows capturing geological elements like

channels, reefs, bars, dikes or differently oriented

facies, while honouring data information.

Isatis, with Impala, powers MPS technique

implementation as it allows generating complex yet

realistic geological patterns in an easy and efficient

way.

1 Acronym for Improved Multiple-point Parallel Algorithm using a List Approach.

Training imagedepicting a channel

MPS simulation in Isatis usingthe previous training image

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Impala algorithm has been optimized in many ways:

It offers a new and efficient strategy for recording

matching configuration. Multi-point statistics are storedin hybrid tree-list structures rather than search trees,

thus resulting in a substantial reduction in memory

requirements and in a higher calculation speed.

Being fully parallelized, it benefits from multi-

processors, which also drastically improves the

calculation performances.

It uses a multi-grid approach to capture structures

within the training image that are defined at different

scales, which avoids considering a huge pattern.

Unlike classical MPS algorithms which tend to

reproduce the global proportions of the training image,Isatis Impala-based MPS uses soft local proportions to

simulate non stationary environment.

It may take into account external continuous

conditioning data which can be diagraphy, seismic or

well information, but also a gradient (i.e. distance to

the coast, distance to specific topographic object, etc.).

This allows refining and enhancing the realistic aspect

of the simulations and enables the simulation of non

stationary deposit environment.

Besides, Isatis provides functionalities to create 2D

training images from imported images and considersobject-based or plurigaussian non conditional simulations

as potential 3D training images.

This Impala-based MPS algorithm complements Isatis

Plurigaussian Simulations (PGS)  already used to

model complex geology geometries. It enriches the

exclusive range of facies modeling techniques Isatis

already offers (including object based simulations like

Boolean, Dead Leaves or Dilution techniques, variogram-

based simulations like SIS, TGS, PGS or training image-

based simulations like the Annealing Simulations).

Isatis MPS Key Points

High performances

Parallel algorithm (CPU performances)

Based on lists (RAM performances) and

trees (speed performances)

Unique features

Multi-grid approach for capturing efficiently

large scale features

Conditioning to hard data (seismic, faciesproportions)

Non–stationarity handled with continuous

auxiliary variables or local proportions

Advantages

Intuitive use of training images (seismic,geological model, analogs, booleansimulations)

Full control of parameters

Proper modeling of non stationarity and localproportions

More flexibility on search template size andnumber of facies

Improved efficiency

Simulation of dykes

with Isatis new MPS

GEOVARIANCES officesFrance - Avon-Fontainebleau - +33 (0)1 6074 9090Australia - Wynnum-Brisbane, QLD - +61 (0)7 3348 5333info@geovariances.com - www.geovariances.com