A COMPARISON OF SELF-ORGANIZING MAPS AND HIERARCHICAL CLUSTER ANALYSIS APPROACHES IN PREDICTING TOTAL ORGANIC CARBON USING INTELLIGENT SYSTEMS

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.

SFIDARI E.; KADKHODAIE A.; SHARIFY M.

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

Journal Paper

Paper Information

Journal: PETROLEUM RESEARCH Year:2013 | Volume:23 | Issue:75 Page(s): 117-130

Download Full-Text

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

Persian Verion

View:

764

Download:

Cites:

Information Journal Paper

Title

A COMPARISON OF SELF-ORGANIZING MAPS AND HIERARCHICAL CLUSTER ANALYSIS APPROACHES IN PREDICTING TOTAL ORGANIC CARBON USING INTELLIGENT SYSTEMS

Author(s)

SFIDARI E. | KADKHODAIE A. | SHARIFY M. | Issue Writer Certificate

Keywords

TOTAL ORGANIC CARBONQ2

SOM CLUSERINGQ2

CLUSTER ANALYSISQ2

ELECTROFACIESQ2

EVALUATING CLUSTERING OUTCOMESQ2

SELFQ2

ORGANIZING MAP NETWORKQ2

Abstract

TOTAL ORGANIC CARBON (TOC) is one of the main parameters for geochemical evaluation of oil and gas source rocks. In this study, we propose a two-step approach to predict TOTAL ORGANIC CARBON content from well log data. Initially, the well log data are classified into a set of ELECTROFACIES (EF). The methods used to characterize and identify EF consist of SELF-organizing maps (SOM) and hierarchical CLUSTER ANALYSIS (HCA). The results obtained from both methods are compared and the best method based on cluster validity tests is chosen for clustering petrophysical data into a certain number of EF. Afterwards, the TOC values are estimated from well log data by using individual artificial neural network (ANN) models constructed for each EF. In the second approach, the TOC data are estimated for the total interval by using a similar ANN model regardless of data clustering and EF determination. The results of two prediction methods are compared to each other and also with a third conventional Δlog R technique. The results show that clustering of a formation into specific units (electrofacies) provides better results in TOC prediction compared to the models constructed for the whole dataset as a single cluster. In addition, intelligent systems are more efficient than the previous conventional techniques based on Δlog R method. The proposed methodology is illustrated using a case study of the world’s largest non-associated gas reservoir, i.e. Iran South Pars Gas Field, located in the Persian Gulf.

Cites

No record.

References

تخمین کل کربن آلی (TOC) توسط روش های شبکه عصبی مصنوعی و Delta log R در میدان گازی پارس جنوبی

Cite

APA: Copy

SFIDARI, E., KADKHODAIE, A., & SHARIFY, M.. (2013). A COMPARISON OF SELF-ORGANIZING MAPS AND HIERARCHICAL CLUSTER ANALYSIS APPROACHES IN PREDICTING TOTAL ORGANIC CARBON USING INTELLIGENT SYSTEMS. PETROLEUM RESEARCH, 23(75), 117-130. SID. https://sid.ir/paper/114974/en

Vancouver: Copy

SFIDARI E., KADKHODAIE A., SHARIFY M.. A COMPARISON OF SELF-ORGANIZING MAPS AND HIERARCHICAL CLUSTER ANALYSIS APPROACHES IN PREDICTING TOTAL ORGANIC CARBON USING INTELLIGENT SYSTEMS. PETROLEUM RESEARCH[Internet]. 2013;23(75):117-130. Available from: https://sid.ir/paper/114974/en

IEEE: Copy

E. SFIDARI, A. KADKHODAIE, and M. SHARIFY, “A COMPARISON OF SELF-ORGANIZING MAPS AND HIERARCHICAL CLUSTER ANALYSIS APPROACHES IN PREDICTING TOTAL ORGANIC CARBON USING INTELLIGENT SYSTEMS,” PETROLEUM RESEARCH, vol. 23, no. 75, pp. 117–130, 2013, [Online]. Available: https://sid.ir/paper/114974/en

Related Journal Papers