1. New Software Tools for Geostatistics: GsTL and Simulacre Nicolas Remy
SCRF meeting 2003

2. Introduction The GSLIB code has shown its limits
Need a new framework which
provides exportable tools: convenient integration into other softwares
implements geostatistics algorithms in their full generality
fosters code reuse

3. Methodology Consider all the algorithms to be implemented
Identify the key concepts they share and those that are unique to each of them
Define the minimal set of properties each concept must have
Implement the algorithms solely in term of the identified concepts and their properties

4. Sequential Simulation
Cdf Estimator
Cdf
Simulation Path
Simulation Path
Neighborhood p
Sampler

5. Sequential Simulation
Simulation path
Cdf sampling
Random path
Random sampling
Spiral path
Metropolis sampling
Cdf estimation
Gaussian Cdf
kriging or cokriging based estimation
Non-parametric cdf:
Indicator kriging
Search Tree
Classification methods (e.g. Neural Network)

6. Concept Requirements Path: has_more_nodes to_next_node cdfEstimator:
estimate_cdf(node, neighbors)
Neighborhood:
find_neighbors(node)
Sampler:
assign_value(node, cdf)
Cdf:
inverse(p)

7. GsTL Algorithms Kriging (simple, ordinary, and with trend)
Cokriging (simple, ordinary, MM1, MM2, LMC)
Sequential simulation (gaussian, indicator, snesim)
Sequential cosimulation (gaussian, indicator, …)
P-field simulation
univariate statistics

8. Main Concepts Simulation Path CDF Estimators Neighborhood Sampler
(random, structured,…)
CDF Estimators
(kriging, NN, search tree,…)
Neighborhood
(ellipsoid, template-based,…)
Sampler
(uniform-random, constant,…)
GeoValue
Correlation Measure
(covariance)
CDF
(gaussian, exponential,
non-parametric, …)

9. Integration Into a Software
Proprietary Grid
Data Structure
(“Tsolid”)
Geovalue
property_value
location
is_informed
Neighborhood
find_neighbors
Path
Iterates on geovalues
GsTL API
SGSIM

10. SGS on a T-Solid
Continuous
Discontinuous

11. SGS on a faulted surface
Continuous
Discontinuous

12. Simulacre: a new geostat software
Algorithm
selection
Parameters for selected algorithm
Visualization panel
algorithm panel

13. Conclusions Two example uses of GsTL
Implementing SGSIM into a commercial software using the GsTL tools
Creating a new geostatistics software from scratch.
It will serve two purposes:
Be a possible GSLIB replacement into which new algorithms will be integrated
Serve as an example of how to use the GsTL tools. Complete source code distributed.

14. Conclusions GsTL is two-fold:
A framework, describing the fundamental properties required by the geostatistics algorithms
a C++ implementation
Using the GsTL framework:
Enables to easily integrate newly developed algorithms when they are added to the GsTL API
Does not mean that what already exists has to be re-written