Sequential Bayesian-Gaussian mixture linear inversion combined with sequential indicator simulation
- Dongyang He (China University of Petroleum-East China) | Xingyao Yin (China University of Petroleum-East China) | Zhaoyun Zong (China University of Petroleum-East China) | Kun Li (China University of Petroleum-East China)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Geostatistics, Impedance, Linear, Facies, Inversion
- 0 in the last 30 days
- 14 since 2007
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Gaussian mixture model can be used to describe the multimodal behaviour of reservoir properties due to their variations within different discrete variables, such as facies. The weights of the Gaussian components represents the probabilities of the discrete variables. However, Bayesian linear inversion based on Gaussian mixture may misclassify discrete variables at some points, which may lead to a bad inversion result. In this study, we consider the spatial variability of discrete variables and combine Gaussian mixture model with the Sequential indicator simulation to determine the weight of each discrete variable in Sequential Bayesian linear inversion problems. We then can obtain the analytical solution of the Bayesian linear inverse problem and simultaneously classify the discrete variables. We certify the feasibility of this method on model data, and then apply this method to actual data collected from an oil field located in China with satisfactory results.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 13
Presentation Type: Poster
|File Size||674 KB||Number of Pages||5|
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