Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics
Abstract Multiple‐point‐based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two‐point (i.e., covariance‐based) geostatistical methods a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-09-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1002/2016GL070348 |
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| _version_ | 1850271835786051584 |
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| author | Knud Skou Cordua Thomas Mejer Hansen Mats Lundh Gulbrandsen Christophe Barnes Klaus Mosegaard |
| author_facet | Knud Skou Cordua Thomas Mejer Hansen Mats Lundh Gulbrandsen Christophe Barnes Klaus Mosegaard |
| author_sort | Knud Skou Cordua |
| collection | DOAJ |
| description | Abstract Multiple‐point‐based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two‐point (i.e., covariance‐based) geostatistical methods are typically applied instead because these methods provide fewer constraints on the Earth model. This study is motivated by the case where 1‐D vertical training images are available through borehole logs, whereas little or no information about horizontal dependencies exists. This problem is solved by developing theory that makes it possible to combine information from multiple‐ and two‐point geostatistics for different directions, leading to a mixed‐point geostatistical model. An example of combining information from the multiple‐point‐based single normal equation simulation algorithm and two‐point‐based sequential indicator simulation algorithm is provided. The mixed‐point geostatistical model is used for conditional sequential simulation based on vertical training images from five borehole logs and a range parameter describing the horizontal dependencies. |
| format | Article |
| id | doaj-art-30e7d68b403841eca676c27fff2b7d61 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2016-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-30e7d68b403841eca676c27fff2b7d612025-08-20T01:52:04ZengWileyGeophysical Research Letters0094-82761944-80072016-09-0143179030903710.1002/2016GL070348Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatisticsKnud Skou Cordua0Thomas Mejer Hansen1Mats Lundh Gulbrandsen2Christophe Barnes3Klaus Mosegaard4Climate and Geophysics Section, Niels Bohr Institute University of Copenhagen Copenhagen DenmarkClimate and Geophysics Section, Niels Bohr Institute University of Copenhagen Copenhagen DenmarkClimate and Geophysics Section, Niels Bohr Institute University of Copenhagen Copenhagen DenmarkDepartment of Geosciences and Environment University of Cergy‐Pontoise Cergy FranceClimate and Geophysics Section, Niels Bohr Institute University of Copenhagen Copenhagen DenmarkAbstract Multiple‐point‐based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two‐point (i.e., covariance‐based) geostatistical methods are typically applied instead because these methods provide fewer constraints on the Earth model. This study is motivated by the case where 1‐D vertical training images are available through borehole logs, whereas little or no information about horizontal dependencies exists. This problem is solved by developing theory that makes it possible to combine information from multiple‐ and two‐point geostatistics for different directions, leading to a mixed‐point geostatistical model. An example of combining information from the multiple‐point‐based single normal equation simulation algorithm and two‐point‐based sequential indicator simulation algorithm is provided. The mixed‐point geostatistical model is used for conditional sequential simulation based on vertical training images from five borehole logs and a range parameter describing the horizontal dependencies.https://doi.org/10.1002/2016GL070348geostatistical prior informationcombining vertical and horizontal informationconditional sequential simulationmultiple‐point geostatisticstwo‐point geostatisticstraining images |
| spellingShingle | Knud Skou Cordua Thomas Mejer Hansen Mats Lundh Gulbrandsen Christophe Barnes Klaus Mosegaard Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics Geophysical Research Letters geostatistical prior information combining vertical and horizontal information conditional sequential simulation multiple‐point geostatistics two‐point geostatistics training images |
| title | Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics |
| title_full | Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics |
| title_fullStr | Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics |
| title_full_unstemmed | Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics |
| title_short | Mixed‐point geostatistical simulation: A combination of two‐ and multiple‐point geostatistics |
| title_sort | mixed point geostatistical simulation a combination of two and multiple point geostatistics |
| topic | geostatistical prior information combining vertical and horizontal information conditional sequential simulation multiple‐point geostatistics two‐point geostatistics training images |
| url | https://doi.org/10.1002/2016GL070348 |
| work_keys_str_mv | AT knudskoucordua mixedpointgeostatisticalsimulationacombinationoftwoandmultiplepointgeostatistics AT thomasmejerhansen mixedpointgeostatisticalsimulationacombinationoftwoandmultiplepointgeostatistics AT matslundhgulbrandsen mixedpointgeostatisticalsimulationacombinationoftwoandmultiplepointgeostatistics AT christophebarnes mixedpointgeostatisticalsimulationacombinationoftwoandmultiplepointgeostatistics AT klausmosegaard mixedpointgeostatisticalsimulationacombinationoftwoandmultiplepointgeostatistics |