Reservoir characterization with sequential Gaussian simulation constrained by diffraction tomography

1994 ◽  
Author(s):  
Tien‐when Lo ◽  
Achmad Bemsawi
Keyword(s):  
Reservoir Characterization ◽  
Sequential Gaussian Simulation ◽  
Diffraction Tomography ◽  
Gaussian Simulation

Assessing of tight reservoir by combining the porosity of geological units, and simulated images of rock types: A case study

2021 ◽  
Author(s):  
Lamia Boussa ◽  
Amar Boudella ◽  
José Almeida
Keyword(s):  
Stochastic Simulation ◽  
3D Model ◽  
Reservoir Characterization ◽  
Rock Type ◽  
Sequential Gaussian Simulation ◽  
Geostatistical Methods ◽  
Rock Types ◽  
Gaussian Simulation ◽  
The Impact

<p>Reservoir characterization and flow studies require accurate inputs of petrophysical properties such as porosity, permeability, water and residual oil saturation and capillary pressure functions. All these parameters are necessary to evaluate, predict and optimize the production of a reservoir.</p><p>This study is the continuity of a previous work that summarize the construction of a net rock aerial map by combining stochastic simulation of rock types and processed seismic data. In this case study; petrophysical data are integrated to construct a 3D model of porosity corresponding to the 3D model of rock type. This is in order to further understand the intricacies of the geostatistical methods used and the impact of the technique on the resulting uncertainty profile</p><p>For the construction of 3D model of porosity corresponding to the 3D model of rock types, a geostatistical workflow encompassing the modelling of experimental variograms and sequential Gaussian simulation (SGS) were used. The geostatsitical methodologies of stochastic simulation such as SGS enabled the generation of several realistic scenarios of constinuous data, such as porosity, within a volume, thus facilitating the association of local probabilities of occurrence of each rock type.</p><p>The resulting porosity image properly combines the available seismic and well data and balance the local and regional uncertainty of the studied reservoir volume.</p><p><strong>Keywords: </strong>Geostatistics, Sequential Gaussian Simulation (SGS), Rock types, Porosity, Uncertainty, Spatial resolution.</p>

Which Path to Choose in Sequential Gaussian Simulation

2017 ◽  
Vol 50 (1) ◽  
pp. 97-120 ◽  
Author(s):  
Raphaël Nussbaumer ◽  
Grégoire Mariethoz ◽  
Erwan Gloaguen ◽  
Klaus Holliger
Keyword(s):  
Sequential Gaussian Simulation ◽  
Gaussian Simulation
Sign in / Sign up

Export Citation Format

Share Document