Geostatistics for optimizing reservoir characterization

Geostatistics provides the most efficient framework to build accurate and reliable static models of reservoirs.

Geostatistics is valuable at all steps of the geomodeling process:

  • Seismic data quality control and enhancement;
  • Time-to-depth conversion and optimal mapping of horizons;
  • Structural uncertainty quantification;
  • Rock-typing;
  • Facies distribution in various geological environments;
  • Petrophysical properties distribution;
  • Uncertainty quantification on Volumetrics.

Geovariances puts its Oil & Gas industry knowledge, continuous innovation and geostatistical expertise at your service to guarantee the quality and reliability of your geological models.

Hear from our customers

"The online training was conducted in 10 sessions of 2 hours over 2 weeks. This gave me the time I needed to fully digest the course between 2 sessions and the opportunity to be critical about what I was doing. I definitely recommend Geovariances online training."


Gabrielle Rumbach, Geologist - Vermilion
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"Thanks to the skills of the trainers and the quality of the documents provided it was a very successful experience, one which I would strongly recommend for other E&P teams."


Antoine Benedini, Head of Geophysics – Integrated projects - Foxtrot International, Ivory Coast
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"Being able to exchange data between Petrel and Isatis in a seamless and quick way is crucial for us. We are very satisfied with the the new capabilities of Isatis-Petrel interface introduced in 2014"


Olinto Gomes de Souza Jr., Geologist - Petrobras, Brazil
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“Best Geostatistical toolbox on earth! I’ve been using Isatis since 1995, and have been affilitated with the Paris School of Mines before that. You can’t beat the reliability and the variety of the tools and methods available to solve literally any spatial problem."


Jeffrey Yarus, Senior Manager of Earth and Reservoir Modeling - Landmark / Halliburton
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“Isatis has proven to be the most effective commercial tool of any I have used for multi-variate data analysis, mapping and modeling over the last 16 years of my career.”


David Garner, Principal Geomodeler - Statoil, Canada
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What is happening in your industry?

News

January 15, 2017

This past summer, Geovariances has conducted a project for an Oil & Gas company whose objective was to get a consistent model of velocity ...

January 12, 2017

Combine MPS and Flumy, Isatis application for channelized system modeling, to ensure model accuracy and realism. See the tests done for a ...

October 25, 2016

Live online training is a perfect way to benefit from interactive training sessions comfortably seated at your keyboard, at the time you n...

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Events

June 12-15, 2017

Geovariances is to exhibit at EAGE 2017 booth #1322. Ask for your presentation of your favorite topics: seismic filtering, time-to-depth c...

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Resources

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Time to Depth Conversion

Time to depth conversion of geological surfaces is critical for structural model building. Quantifying the uncertainty attached to the conversion is also of primordial importance for assessing GRV uncertainties. Traditional velocity models used in time to depth conversion could benefit from geostatistical techniques used in data integration. The advantage of using geostatistical methods is that they fit the data in one step and allow quantifying the uncertainty attached to the prediction by mean of the generation of equiprobable realizations.

Through Geovariances long-lasting experience in geostatistical depth conversion studies, learn how geostatistics helps you improve the accuracy of your reservoir structural model and assess the uncertainties on surfaces.

Mapping with auxiliary data

Through this white paper, discover how you canimprove significantly map reliability and quality by incorporating various sources of information in the interpolation process.

This document details the different methods for assimilating various sources of information, taking into account the reliability of each source and how the uncertainty associated with any mapping result can be estimated and reduced.

Geological Facies Simulations

Whatever the resource involved – oil & gas, coal or metallic resources – capturing the variability of the geological parameters is essential at the modelling stage as the characteristics of the distributions of key parameters conditioning the resource recovery (e.g. rock properties, grades, etc.) are informed by the geological context. A large variety of simulation techniques is available to model geological facies.

Through Geovariances strong experience in developing successfully simulation strategies for different geological environments (e.g. kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, hydrothermal type deposits, etc.), learn how to choose the best facies modelling technique according to the specific geological depositional environment. Analyse each method advantages and drawbacks.

How to Capture Trend Uncertainty with Bayesian Kriging

In presence of trends, kriging with Bayesian drift bridges the gap between the traditional kriging with external drift and a simple kriging of the residuals, allowing a better trend control. The method has wide ranges of applications, an important one being its ability to deal with time to depth conversion.

Through this white paper, find out how the technique benefits from the prior knowledge gained from similar fields regarding the trend shape to produce coherent estimates, especially when the data are sparse and traditional geostatistical data analysis may lack robustness.

Seismic Filtering

The quality of seismic volumes is critical in building reliable reservoir models. Seismic data are often polluted by acquisition or processing artifacts which may have strong impact on subsequent seismic processing or interpretation. Geostatistics allows filtering efficiently seismic noise and artifacts without modifying the signal.

Through Geovariances strong experience in geostatistical seismic filtering,learn how to improve your seismic data quality to facilitate interpretation and gain time in the many processes that result.

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