�ݺ�ߣshows by User: Geovariances

�ݺ�ߣshows by User: Geovariances / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Geovariances / Fri, 20 Jun 2014 05:19:23 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Geovariances Conditioning static models with connectivity information /Geovariances/geovariancesconnectivity-analysis geovariancesconnectivityanalysis-140620051923-phpapp02
Production History Match optimization requires a good consistency between geological static model and reservoir model. This is the case when the wells that have been identified as connected from dynamic synthesis are really connected in the model. Connectivity Analysis is a way to QC a static model and to check its consistency with dynamic data at a very early stage. It allows identifying potential issues before starting time consuming flow simulations. It facilitates communication between geomodelers and reservoir engineers, by defining and quantifying the impact of geological parameters on wells connection. Connectivity Analysis also provides some solutions to fix geological model inconsistencies and to ensure that the static model honors connectivity information.]]>
Production History Match optimization requires a good consistency between geological static model and reservoir model. This is the case when the wells that have been identified as connected from dynamic synthesis are really connected in the model. Connectivity Analysis is a way to QC a static model and to check its consistency with dynamic data at a very early stage. It allows identifying potential issues before starting time consuming flow simulations. It facilitates communication between geomodelers and reservoir engineers, by defining and quantifying the impact of geological parameters on wells connection. Connectivity Analysis also provides some solutions to fix geological model inconsistencies and to ensure that the static model honors connectivity information.]]> Fri, 20 Jun 2014 05:19:23 GMT /Geovariances/geovariancesconnectivity-analysis Geovariances@slideshare.net(Geovariances) Conditioning static models with connectivity information Geovariances Production History Match optimization requires a good consistency between geological static model and reservoir model. This is the case when the wells that have been identified as connected from dynamic synthesis are really connected in the model. Connectivity Analysis is a way to QC a static model and to check its consistency with dynamic data at a very early stage. It allows identifying potential issues before starting time consuming flow simulations. It facilitates communication between geomodelers and reservoir engineers, by defining and quantifying the impact of geological parameters on wells connection. Connectivity Analysis also provides some solutions to fix geological model inconsistencies and to ensure that the static model honors connectivity information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geovariancesconnectivityanalysis-140620051923-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Production History Match optimization requires a good consistency between geological static model and reservoir model. This is the case when the wells that have been identified as connected from dynamic synthesis are really connected in the model. Connectivity Analysis is a way to QC a static model and to check its consistency with dynamic data at a very early stage. It allows identifying potential issues before starting time consuming flow simulations. It facilitates communication between geomodelers and reservoir engineers, by defining and quantifying the impact of geological parameters on wells connection. Connectivity Analysis also provides some solutions to fix geological model inconsistencies and to ensure that the static model honors connectivity information.

Conditioning static models with connectivity information from Geovariances

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1 3D oil reservoir model uncertainty: model-derived uncertainty or uncertainty about models. /Geovariances/3-d-modeluncertainty 3dmodeluncertainty-131216102412-phpapp01
Find out more about the importance of the model uncertainty quantification according to the reservoir study stage. The presentation highlights the difference between the uncertainty quantification and the capacity of prediction of a model. Model uncertainty is due to various factors such as the lack of precise data, the use of imprecise data or the impossibility to conceive with certainty a conceptual model. Some of the factors can be accounted for (known source of uncertainty) and therefore be captured in a model, leading to classical uncertainty quantification. Other factors cannot be accounted for (unknown source of uncertainty), therefore limiting the capacity of prediction of the model.]]>
Find out more about the importance of the model uncertainty quantification according to the reservoir study stage. The presentation highlights the difference between the uncertainty quantification and the capacity of prediction of a model. Model uncertainty is due to various factors such as the lack of precise data, the use of imprecise data or the impossibility to conceive with certainty a conceptual model. Some of the factors can be accounted for (known source of uncertainty) and therefore be captured in a model, leading to classical uncertainty quantification. Other factors cannot be accounted for (unknown source of uncertainty), therefore limiting the capacity of prediction of the model.]]> Mon, 16 Dec 2013 10:24:12 GMT /Geovariances/3-d-modeluncertainty Geovariances@slideshare.net(Geovariances) 3D oil reservoir model uncertainty: model-derived uncertainty or uncertainty about models. Geovariances Find out more about the importance of the model uncertainty quantification according to the reservoir study stage. The presentation highlights the difference between the uncertainty quantification and the capacity of prediction of a model. Model uncertainty is due to various factors such as the lack of precise data, the use of imprecise data or the impossibility to conceive with certainty a conceptual model. Some of the factors can be accounted for (known source of uncertainty) and therefore be captured in a model, leading to classical uncertainty quantification. Other factors cannot be accounted for (unknown source of uncertainty), therefore limiting the capacity of prediction of the model. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3dmodeluncertainty-131216102412-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Find out more about the importance of the model uncertainty quantification according to the reservoir study stage. The presentation highlights the difference between the uncertainty quantification and the capacity of prediction of a model. Model uncertainty is due to various factors such as the lack of precise data, the use of imprecise data or the impossibility to conceive with certainty a conceptual model. Some of the factors can be accounted for (known source of uncertainty) and therefore be captured in a model, leading to classical uncertainty quantification. Other factors cannot be accounted for (unknown source of uncertainty), therefore limiting the capacity of prediction of the model.

3D oil reservoir model uncertainty: model-derived uncertainty or uncertainty about models. from Geovariances

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1 Geostatistics for radiological characterization and sampling optimization /slideshow/added/26698886 wm2013geostatisticsdesnoyers-130930102407-phpapp02
Find out in a few slides why geostatistics is essential to a reliable and precise radiological characterization. This presentation has been made by our expert during WM 2013 Conference in Phoenix.]]>
Find out in a few slides why geostatistics is essential to a reliable and precise radiological characterization. This presentation has been made by our expert during WM 2013 Conference in Phoenix.]]> Mon, 30 Sep 2013 10:24:07 GMT /slideshow/added/26698886 Geovariances@slideshare.net(Geovariances) Geostatistics for radiological characterization and sampling optimization Geovariances Find out in a few slides why geostatistics is essential to a reliable and precise radiological characterization. This presentation has been made by our expert during WM 2013 Conference in Phoenix. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wm2013geostatisticsdesnoyers-130930102407-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Find out in a few slides why geostatistics is essential to a reliable and precise radiological characterization. This presentation has been made by our expert during WM 2013 Conference in Phoenix.

Geostatistics for radiological characterization and sampling optimization from Geovariances

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1 Seismic QC & Filtering with Geostatistics /Geovariances/geovariancesseismic-qc-filtering geovariancesseismicqcfiltering-130627111935-phpapp01
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. Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.]]>
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. Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.]]> Thu, 27 Jun 2013 11:19:35 GMT /Geovariances/geovariancesseismic-qc-filtering Geovariances@slideshare.net(Geovariances) Seismic QC & Filtering with Geostatistics Geovariances 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. Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geovariancesseismicqcfiltering-130627111935-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 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. Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.

Seismic QC & Filtering with Geostatistics from Geovariances

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1 https://cdn.slidesharecdn.com/profile-photo-Geovariances-48x48.jpg?cb=1674745245 Founded in 1986, Geovariances develops and provides premium geostatistics software solutions and services for mining and energy companies to assess resources with greater accuracy and confidence, environmental consultancies to design more effective remediation and decommissioning, or geotechnical engineering consultancies to improve their knowledge of the subsurface to manage risks better and make effective decisions. We help our clients build robust natural resource and environmental models by giving them easy access to a complete set of powerful and intuitive statistical and geostatistical tools. www.geovariances.com/en/ https://cdn.slidesharecdn.com/ss_thumbnails/geovariancesconnectivityanalysis-140620051923-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds Geovariances/geovariancesconnectivity-analysis Conditioning static mo... https://cdn.slidesharecdn.com/ss_thumbnails/3dmodeluncertainty-131216102412-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds Geovariances/3-d-modeluncertainty 3D oil reservoir model... https://cdn.slidesharecdn.com/ss_thumbnails/wm2013geostatisticsdesnoyers-130930102407-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/added/26698886 Geostatistics for radi...