Isatis.neo gathers a unique set of geostatistical simulation algorithms

December 8, 2020

Geovariances, with Isatis.neo, brings the most performing geostatistical simulation algorithm to the market: SPDE. This new approach adds to widely used simulation techniques available today in Isatis.neo, giving users access to the broadest portfolio of geostatistical simulation techniques in a single software solution and providing the basis for thorough risk analysis.

SPDE runs simulations faster than any other algorithms

Isatis.neo 2020.10 offers the industry the most powerful conditional simulation algorithm on the market, SPDE. Unique to Isatis.neo, this innovative approach allows addressing the multiple issues that users face today:  integrate more and more data, boost productivity, and come up with an answer fast and, at the same time, enriched with an assessment of the uncertainty that can be attached to that answer.

The tests conducted at Geovariances have shown that users can obtain simulation realizations up to 50 times faster in 2D and 3 times faster in 3D than running the standard Turning Bands (TBS) method. The key ingredient to allow that quantum leap in performance is the solving of Stochastic Partial Differential Equations, hence the name given to the new algorithm.

Speed is not the only strength of SPDE-based simulations. Applying this technique, users do not need to worry anymore about the estimation neighborhood size as it can easily digest several hundreds of thousands of data points. It can also take into account local anisotropies to refine estimations locally.

This unequaled algorithm derives from a 2-year research consortium Geovariances has conducted in partnership with the Center for Geostatistics from MINES ParisTech and major mining companies, including Anglo American, BHP, Eramet, Kinross, Newcrest, and Orano.

Popular simulation methodologies are also available

Isatis.neo 2020.10 also adds the classical and widely used Sequential Gaussian Simulations (SGS) besides the Turning Bands Simulations (TBS). This latest approach has two key advantages. First, it is a robust methodology that respects input data statistics. Secondly, it enables computation multithreading to achieve the best performance.

Two specific applications deliver realistic facies models

Two different geological facies modeling approaches have been made available in the newest version of Isatis.neo.

The Plurigaussian Simulations, already available in Isatis, the predecessor of Isatis.neo, has been fully redeveloped and redesigned to make it far more accessible. It is based on an original algorithm developed by the Center for Geostatistics from MINES ParisTech that fully automates the fitting of the underlying Gaussian model, the trickiest part of the method, and the inference of the lithotype rule (i.e., the model of facies relationships) so that the variogram is adjusted at best.

The Plurigaussian simulations are particularly suitable for modeling orderly geological sequences and multiphase phenomena, where diagenetic processes alter sedimentation.

Besides, Isatis.neo 2020.10 now offers advanced Multiple-point Statistics (MPS) with the DeeSse algorithm from the University of Neuch√Ętel in Switzerland ( The approach’s principle is to mimic a reference or training image. This image could be an analog if the geological environment is known. Users can also start from a simple theoretical image and deform it through scaling and rotation, or applying a trend to build a more elaborate scheme. They can also force some grid cells to connect each other or each simulation realization to respect input anisotropies or facies proportions. All that makes the DeeSse implementation very flexible, enabling the modeling of complex relationships between facies. DeeSse also applies to multivariate issues.

The importance of the geostatistical simulations

The kriging interpolation algorithm is known to produce a smoothed image of the phenomenon you are studying, delivering the most probable reality values. It is not relevant to tackle risk analysis issues as it does not consider the phenomenon’s full variability. It is where geostatistical simulations must come in. They deliver many possible versions of reality from which you calculate the distribution of possible values (and associated min, max, mean, quantiles). They enable the inference of optimistic and pessimistic scenarios for a given confidence interval and the probability for the values to exceed a cutoff or a threshold, allowing decision-makers to make a more informed decision.

Geovariances has been pioneering these techniques for decades. We have substantial experience in applying simulations to address various issues such as assessing population exposure to a given air pollution level, the probability of attaining a geological layer when drilling, the expected oil volumes in a reservoir, or the ore’s grade variability arriving at the mill.



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About Isatis.neo

Isatis.neo leverages the technology of its predecessor and reference software, Isatis. It offers, in an easy-to-use interface, a wide choice of statistical and geostatistical techniques for data analysis, mapping, block modeling, resource estimation, and classification, as well as the ability to perform accurate risk analysis. It is a scalable software solution that meets the whole resource team’s needs. It allows resource managers to set up estimation workflows that will be automated to streamline mine geologists’ daily tasks.

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About Geovariances

Founded in 1986 and headquartered in France with offices in Brazil, Chile, and Australia, Geovariances has been a world leader in applied geostatistics for 35 years. The company provides data analysis, mapping, resource estimation, reservoir modeling, and risk analysis to clients worldwide through geostatistics-based software solutions, training, and consulting services. Employing only highly qualified specialists, Geovariances is a frontrunner in ensuring clients with reliable and scientifically accurate solutions. Geovariances’ primary markets include the Mining, Upstream Oil and Gas, Nuclear Decommissioning and Contaminated Sites industries, and many other sectors.

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