ANFIS Rules Driven Integrated Seismic and Petrophysical Facies Analysis

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mirzakhanian M.; Hashemi H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of the Earth and Space Physics Year:2022 | Volume:47 | Issue:4 Page(s): 133-141

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

ANFIS Rules Driven Integrated Seismic and Petrophysical Facies Analysis

Author(s)

Mirzakhanian M. | Hashemi H. | Issue Writer Certificate

Keywords

Seismic attributes

Extended elastic impedance

Facies analysis

Adaptive neuro-fuzzy inference system

Abstract

Different learning methods have been used to recognize seismic facies and reservoir characterization using Seismic attributes. One of the significant issues in automatic Facies analysis is to relate the seismic data to facies properties using the well data. According to previous studies, the role of attributes is more significant than the learning method for automatic classification. The proposed method uses supervised selection of Seismic attributes for automatic Facies analysis. Extended elastic impedances (EEI) at different angles as Seismic attributes are being increasingly utilized in both seismic Facies analysis and reservoir characterization. They are representative of elastic parameters of rocks appropriately. In the presented method, proper EEI Seismic attributes are selected after a feasibility study using petro-physical logs, and EEI template analysis of the well data. Adaptive neuro-fuzzy inference system (ANFIS) is applied to the fuzzy coded data of the well facies to train an automatic model to predict facies from the seismic data. Subsequently, the same particular EEI attributes are prepared. The EEI attributes from the seismic data are inputs for the trained ANIFIS model to perform seismic Facies analysis. In this method, the seismic facies and the well facies are compatible. Only one well data can be sufficient for the well analysis stage and well facies clustering. The proposed method is applied on 3D prestack seismic data located in Abadan plain to discriminate hydrocarbon interval of Sarvak Formation. The results reveal that the supervised selection of attributes and fuzzy concepts present remarkable ability in dealing with imprecise seismic Facies analysis and reservoir characterization.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans