MultiplePoint Simulation with an Existing Reservoir Model as Training Image
 L. Y. Hu,
 Y. Liu,
 C. Scheepens,
 A. W. Shultz,
 R. D. Thompson
 … show all 5 hide
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The multiplepoint simulation (MPS) method has been increasingly used to describe the complex geologic features of petroleum reservoirs. The MPS method is based on multiplepoint statistics from training images that represent geologic patterns of the reservoir heterogeneity. The traditional MPS algorithm, however, requires the training images to be stationary in space, although the spatial distribution of geologic patterns/features is usually nonstationary. Building geologically realistic but statistically stationary training images is somehow contradictory for reservoir modelers. In recent research on MPS, the concept of a training image has been widely extended. The MPS approach is no longer restricted by the size or the stationarity of training images; a training image can be a small geometrical element or a fullfield reservoir model. In this paper, the different types of training images and their corresponding MPS algorithms are first reviewed. Then focus is placed on a case where a reservoir model exists, but needs to be conditioned to well data. The existing model can be built by processbased, objectbased, or any other type of reservoir modeling approach. In general, the geologic patterns in a reservoir model are constrained by depositional environment, seismic data, or other trend maps. Thus, they are nonstationary, in the sense that they are location dependent. A new MPS algorithm is proposed that can use any existing model as training image and condition it to well data. In particular, this algorithm is a practical solution for conditioning geologicprocessbased reservoir models to well data.
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 Title
 MultiplePoint Simulation with an Existing Reservoir Model as Training Image
 Journal

Mathematical Geosciences
Volume 46, Issue 2 , pp 227240
 Cover Date
 20140201
 DOI
 10.1007/s1100401394888
 Print ISSN
 18748961
 Online ISSN
 18748953
 Publisher
 Springer Berlin Heidelberg
 Additional Links
 Topics
 Keywords

 Training image
 Nonstationarity
 Geologicprocessbased model
 Kernel pdf
 Conditional probability
 Geostatistics
 Industry Sectors
 Authors

 L. Y. Hu ^{(1)}
 Y. Liu ^{(1)}
 C. Scheepens ^{(1)}
 A. W. Shultz ^{(1)}
 R. D. Thompson ^{(1)}
 Author Affiliations

 1. ConocoPhillips Geosciences & Reservoir Engineering Technology, Houston, TX, 77079, USA