• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Earth Science
  • Department of Earth Science
  • View Item
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Earth Science
  • Department of Earth Science
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Quantitative interpretation using inverse rock-physics modeling on AVO data

Jensen, Erling Hugo; Johansen, Tor Arne; Avseth, Per; Bredesen, Kenneth
Peer reviewed, Journal article
Published version
Thumbnail
View/Open
PDF (4.586Mb)
URI
https://hdl.handle.net/1956/15678
Date
2016-08
Metadata
Show full item record
Collections
  • Department of Earth Science [670]
Original version
https://doi.org/10.1190/tle35080677.1
Abstract
Quantitative seismic interpretation has become an important and critical technology for improved hydrocarbon exploration and production. However, this is typically a resource-demanding process that requires information from several well logs, building a representative velocity model, and, of course, high-quality seismic data. Therefore, it is very challenging to perform in an exploration or appraisal phase with limited well control. Conventional seismic interpretation and qualitative analysis of amplitude variations with offset (AVO) are more common tools in these phases. Here, we demonstrate a method for predicting quantitative reservoir properties and facies using AVO data and a rock-physics model calibrated with well-log data. This is achieved using a probabilistic inversion method that combines stochastic inversion with Bayes' theorem. The method honors the nonuniqueness of the problem and calculates probabilities for the various solutions. To evaluate the performance of the method and the quality of the results, we compare them with similar reservoir property predictions obtained using the same method on seismic-inversion data. Even though both approaches use the same method, the input data have some fundamental differences, and some of the modeling assumptions are not the same. Considering these differences, the two approaches produce comparable predictions. This opens up the possibility to perform quantitative interpretation in earlier phases than what is common today, and it might provide the analyst with better control of the various assumptions that are introduced in the work process.
Publisher
Society of Exploration Geophysicists
Journal
The Leading Edge
Copyright
Copyright Society of Exploration Geophysicists. All rights Reserved.

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit