Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Porosity from seismic data: A geostatistical approach

Porosity from seismic data: A geostatistical approach <jats:p> Using a geostatistical technique called cokriging, the areal distribution of porosity is estimated first in a numerically simulated reservoir model, then in an oil‐bearing channel‐sand of Alberta, Canada. The cokriging method consistently integrates 3-D reflection seismic data with well measurements of the porosity and provides error‐qualified, linear mean square estimates of this parameter. In contrast to traditional seismically assisted porosity mapping techniques that treat the data as spatially independent observations, the geostatistical approach uses spatial autocorrelation and crosscorrelation functions to model the lateral variations of the reservoir properties. In the simulated model, the experimental root‐mean square porosity error with cokriging is 50 percent smaller than the error in predictions relying on a least‐squares regression of porosity on seismically derived transit time in the reservoir interval. In the Alberta reservoir, a cross‐validation study at the wells demonstrates that the cokriging procedure is 20 percent more accurate, in a mean square sense, than a standard regression method, which accounts only for local correlations between porosity and seismically derived impedances. In both cases, cokriging capitalizes on areally dense seismic measurements that are indirectly related to porosity. As a result, when compared to estimates obtained by interpolating the well data, this technique considerably improves the spatial description of porosity in areas of sparse well control. </jats:p> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png GEOPHYSICS CrossRef

Porosity from seismic data: A geostatistical approach

GEOPHYSICS , Volume 53 (10): 1263-1275 – Oct 1, 1988

Porosity from seismic data: A geostatistical approach


Abstract

<jats:p> Using a geostatistical technique called cokriging, the areal distribution of porosity is estimated first in a numerically simulated reservoir model, then in an oil‐bearing channel‐sand of Alberta, Canada. The cokriging method consistently integrates 3-D reflection seismic data with well measurements of the porosity and provides error‐qualified, linear mean square estimates of this parameter. In contrast to traditional seismically assisted porosity mapping techniques that treat the data as spatially independent observations, the geostatistical approach uses spatial autocorrelation and crosscorrelation functions to model the lateral variations of the reservoir properties. In the simulated model, the experimental root‐mean square porosity error with cokriging is 50 percent smaller than the error in predictions relying on a least‐squares regression of porosity on seismically derived transit time in the reservoir interval. In the Alberta reservoir, a cross‐validation study at the wells demonstrates that the cokriging procedure is 20 percent more accurate, in a mean square sense, than a standard regression method, which accounts only for local correlations between porosity and seismically derived impedances. In both cases, cokriging capitalizes on areally dense seismic measurements that are indirectly related to porosity. As a result, when compared to estimates obtained by interpolating the well data, this technique considerably improves the spatial description of porosity in areas of sparse well control. </jats:p>

Loading next page...
 
/lp/crossref/porosity-from-seismic-data-a-geostatistical-approach-uyruJxp3JA

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
CrossRef
ISSN
0016-8033
DOI
10.1190/1.1442404
Publisher site
See Article on Publisher Site

Abstract

<jats:p> Using a geostatistical technique called cokriging, the areal distribution of porosity is estimated first in a numerically simulated reservoir model, then in an oil‐bearing channel‐sand of Alberta, Canada. The cokriging method consistently integrates 3-D reflection seismic data with well measurements of the porosity and provides error‐qualified, linear mean square estimates of this parameter. In contrast to traditional seismically assisted porosity mapping techniques that treat the data as spatially independent observations, the geostatistical approach uses spatial autocorrelation and crosscorrelation functions to model the lateral variations of the reservoir properties. In the simulated model, the experimental root‐mean square porosity error with cokriging is 50 percent smaller than the error in predictions relying on a least‐squares regression of porosity on seismically derived transit time in the reservoir interval. In the Alberta reservoir, a cross‐validation study at the wells demonstrates that the cokriging procedure is 20 percent more accurate, in a mean square sense, than a standard regression method, which accounts only for local correlations between porosity and seismically derived impedances. In both cases, cokriging capitalizes on areally dense seismic measurements that are indirectly related to porosity. As a result, when compared to estimates obtained by interpolating the well data, this technique considerably improves the spatial description of porosity in areas of sparse well control. </jats:p>

Journal

GEOPHYSICSCrossRef

Published: Oct 1, 1988

There are no references for this article.