CONDITIONAL SIMULATION OF COMPLEX GEOLOGICAL STRUCTURES USING MULTIPLE-POINT STATISTICS
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dc.contributor.author | Strebelle S. | |
dc.date.accessioned | 2021-04-16T04:39:54Z | |
dc.date.available | 2021-04-16T04:39:54Z | |
dc.date.issued | 2002 | |
dc.identifier | https://www.elibrary.ru/item.asp?id=950024 | |
dc.identifier.citation | Mathematical Geology, 2002, 34, 1, 1-21 | |
dc.identifier.issn | 0882-8121 | |
dc.identifier.uri | https://repository.geologyscience.ru/handle/123456789/27882 | |
dc.description.abstract | In many earth sciences applications, the geological objects or structures to be reproduced are curvilinear, e.g., sand channels in a clastic reservoir. Their modeling requires multiple-point statistics involving jointly three or more points at a time, much beyond the traditional two-point variogram statistics. Actual data from the field being modeled, particularly if it is subsurface, are rarely enough to allow inference of such multiple-point statistics. The approach proposed in this paper consists of borrowing the required multiple-point statistics from training images depicting the expected patterns of geological heterogeneities. Several training images can be used, reflecting different scales of variability and styles of heterogeneities. The multiple-point statistics inferred from these training image(s) are exported to the geostatistical numerical model where they are anchored to the actual data, both hard and soft, in a sequential simulation mode. The algorithm and code developed are tested for the simulation of a fluvial hydrocarbon reservoir with meandering channels. The methodology proposed appears to be simple (multiple-point statistics are scanned directly from training images), general (any type of random geometry can be considered), and fast enough to handle large 3D simulation grids. | |
dc.subject | GEOSTATISTICS | |
dc.subject | STOCHASTIC SIMULATION | |
dc.subject | TRAINING IMAGE | |
dc.subject | RANDOM GEOMETRY | |
dc.title | CONDITIONAL SIMULATION OF COMPLEX GEOLOGICAL STRUCTURES USING MULTIPLE-POINT STATISTICS | |
dc.type | Статья |
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