Probabilistic Reservoir Property Modelling Jointly Constrained by 3D Seismic Data and Hydraulic Unit Analysis

Author(s):  
Mohammad Emami Niri ◽  
David E Lumley
2016 ◽  
Vol 19 (02) ◽  
pp. 253-264 ◽  
Author(s):  
M. Emami Niri ◽  
D. E. Lumley

Summary We present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock-physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic-unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D-seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.


2012 ◽  
Vol 2012 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Peter Kovesi ◽  
Ben Richardson ◽  
Eun-Jung Holden ◽  
Jeffrey Shragge

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