PERMEABILITY PREDICTION FROM WIRELINE LOGS USING ARTIFICIAL NEURAL NETWORKS

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.

HABIBIAN B.; NABI BID HENDI M.; KAZENNAJAD E.

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

Journal Paper

Paper Information

Journal: Journal of the Earth and Space Physics Year:2005 | Volume:31 | Issue:1 Page(s): 79-86

Download Full-Text

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

Persian Verion

View:

892

Download:

Cites:

Information Journal Paper

Title

PERMEABILITY PREDICTION FROM WIRELINE LOGS USING ARTIFICIAL NEURAL NETWORKS

Author(s)

HABIBIAN B. | NABI BID HENDI M. | KAZENNAJAD E. | Issue Writer Certificate

Keywords

PERMEABILITYQ4

ARTIFICIAL NEURAL NETWORKSQ2

BACK PROPAGATION NETWORKQ4

Abstract

The study presented here is an artificial neural network modeling for prediction of PERMEABILITY in one of the limestone reservoirs in the south of Iran. The PERMEABILITY ANN is a three layer network. We have used two separate ANNs to model PERMEABILITY. Initially, density, gamma ray, neutron and sonic logs were applied for input. Then depth data related to these data were added to the input; the results of the two networks have then been compared. This comparison has shown that in the second state the accuracy of the model has been improved significantly.

Cites

SIMULATION OF PHOTOELECTRIC LOG IN OIL-BEARING FORMATION USING ARTIFICIAL NEURAL NETWORK

References

No record.

Cite

APA: Copy

HABIBIAN, B., NABI BID HENDI, M., & KAZENNAJAD, E.. (2005). PERMEABILITY PREDICTION FROM WIRELINE LOGS USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF THE EARTH AND SPACE PHYSICS, 31(1), 79-86. SID. https://sid.ir/paper/80522/en

Vancouver: Copy

HABIBIAN B., NABI BID HENDI M., KAZENNAJAD E.. PERMEABILITY PREDICTION FROM WIRELINE LOGS USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF THE EARTH AND SPACE PHYSICS[Internet]. 2005;31(1):79-86. Available from: https://sid.ir/paper/80522/en

IEEE: Copy

B. HABIBIAN, M. NABI BID HENDI, and E. KAZENNAJAD, “PERMEABILITY PREDICTION FROM WIRELINE LOGS USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF THE EARTH AND SPACE PHYSICS, vol. 31, no. 1, pp. 79–86, 2005, [Online]. Available: https://sid.ir/paper/80522/en

Related Journal Papers