Geostatistical Seismic Inversion Using Well Log Constraints
Information about reservoir properties usually comes from two sources: seismic data and well logs. The former provide an indirect, low resolution image of rock velocity and density. The latter provide direct, high resolution (but laterally sparse) sampling of these and other rock parameters. An i...
| Main Authors: | , , , |
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| Format: | Technical Report |
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Massachusetts Institute of Technology. Earth Resources Laboratory
2012
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| Online Access: | http://hdl.handle.net/1721.1/75425 |
| _version_ | 1811084332264062976 |
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| author | Kane, Jonathan Rodi, William Herrmann, Felix Toksoz, M. Nafi |
| author2 | Massachusetts Institute of Technology. Earth Resources Laboratory |
| author_facet | Massachusetts Institute of Technology. Earth Resources Laboratory Kane, Jonathan Rodi, William Herrmann, Felix Toksoz, M. Nafi |
| author_sort | Kane, Jonathan |
| collection | MIT |
| description | Information about reservoir properties usually comes from two sources: seismic data
and well logs. The former provide an indirect, low resolution image of rock velocity
and density. The latter provide direct, high resolution (but laterally sparse) sampling
of these and other rock parameters. An important problem in reservoir characterization
is how best to combine these data sets, allowing the well information to constrain the
seismic inversion and, conversely, using the seismic data to spatially interpolate and
extrapolate the well logs.
We have developed a seismic/well log inversion method that combines geostatistical
methods for well log interpolation (i.e., kriging) with a Monte Carlo search technique
for seismic inversion. Our method follows the approach used by Haas and Dubrule
(1994) in their sequential inversion algorithm. Kriging is applied to the well data to
obtain velocity estimates and their variances for use as a priori constraints in the seismic inversion. Further, inversion of a complete 2-D seismic section is performed one trace at a time. The velocity profiles derived from previous seismic traces are incorporated as "pseudo well logs" in subsequent applications of kriging. Our version of this algorithm employs a more efficient Monte Carlo search algorithm in the seismic inversion step, and moves progressively away from the wells so as to minimize the kriging variance at each step. Numerical experiments with synthetic data demonstrate the viability of our seismic/well data inversion scheme. |
| first_indexed | 2024-09-23T12:49:02Z |
| format | Technical Report |
| id | mit-1721.1/75425 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:49:02Z |
| publishDate | 2012 |
| publisher | Massachusetts Institute of Technology. Earth Resources Laboratory |
| record_format | dspace |
| spelling | mit-1721.1/754252019-04-12T20:32:24Z Geostatistical Seismic Inversion Using Well Log Constraints Kane, Jonathan Rodi, William Herrmann, Felix Toksoz, M. Nafi Massachusetts Institute of Technology. Earth Resources Laboratory Kane, Jonathan Rodi, William Herrmann, Felix Toksoz, M. Nafi Inversion Logging Information about reservoir properties usually comes from two sources: seismic data and well logs. The former provide an indirect, low resolution image of rock velocity and density. The latter provide direct, high resolution (but laterally sparse) sampling of these and other rock parameters. An important problem in reservoir characterization is how best to combine these data sets, allowing the well information to constrain the seismic inversion and, conversely, using the seismic data to spatially interpolate and extrapolate the well logs. We have developed a seismic/well log inversion method that combines geostatistical methods for well log interpolation (i.e., kriging) with a Monte Carlo search technique for seismic inversion. Our method follows the approach used by Haas and Dubrule (1994) in their sequential inversion algorithm. Kriging is applied to the well data to obtain velocity estimates and their variances for use as a priori constraints in the seismic inversion. Further, inversion of a complete 2-D seismic section is performed one trace at a time. The velocity profiles derived from previous seismic traces are incorporated as "pseudo well logs" in subsequent applications of kriging. Our version of this algorithm employs a more efficient Monte Carlo search algorithm in the seismic inversion step, and moves progressively away from the wells so as to minimize the kriging variance at each step. Numerical experiments with synthetic data demonstrate the viability of our seismic/well data inversion scheme. Massachusetts Institute of Technology. Borehole Acoustics and Logging Consortium Massachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation Consortium 2012-12-12T18:30:52Z 2012-12-12T18:30:52Z 1999 Technical Report http://hdl.handle.net/1721.1/75425 Earth Resources Laboratory Industry Consortia Annual Report;1999-10 application/pdf Massachusetts Institute of Technology. Earth Resources Laboratory |
| spellingShingle | Inversion Logging Kane, Jonathan Rodi, William Herrmann, Felix Toksoz, M. Nafi Geostatistical Seismic Inversion Using Well Log Constraints |
| title | Geostatistical Seismic Inversion Using Well Log Constraints |
| title_full | Geostatistical Seismic Inversion Using Well Log Constraints |
| title_fullStr | Geostatistical Seismic Inversion Using Well Log Constraints |
| title_full_unstemmed | Geostatistical Seismic Inversion Using Well Log Constraints |
| title_short | Geostatistical Seismic Inversion Using Well Log Constraints |
| title_sort | geostatistical seismic inversion using well log constraints |
| topic | Inversion Logging |
| url | http://hdl.handle.net/1721.1/75425 |
| work_keys_str_mv | AT kanejonathan geostatisticalseismicinversionusingwelllogconstraints AT rodiwilliam geostatisticalseismicinversionusingwelllogconstraints AT herrmannfelix geostatisticalseismicinversionusingwelllogconstraints AT toksozmnafi geostatisticalseismicinversionusingwelllogconstraints |