Paper Information

Journal:   PETROLEUM RESEARCH   WINTER 2016 , Volume 25 , Number 85-2; Page(s) 100 To 110.
 
Paper: 

ESTIMATION OF TOTAL ORGANIC CARBON LOG USING GGEOCHEMICAL AND PETROPHYSICAL DATA BY ARTIFICIAL NEURAL NETWORKS IN AZADEGAN OIL FIELD

 
 
Author(s):  GHOLIPOUR SIROUS*, KADKHODAIE ALI, KAMALI MOHAMMAD REZA
 
* DEPARTMENT OF GEOLOGY, SCIENCE AND RESEARCH BRANCH, ISLAMIC AZAD UNIVERSITY, TEHRAN
 
Abstract: 

The amount of total organic carbon (TOC) is one of the major geochemical parameters, which is used to evaluate hydrocarbon generation potential of source rocks. Measurement of such an important parameter requires performing tests on small-scale drill cuttings which is too expensive and time-consuming. Meanwhile, it is measured using a limited number of samples. However, petrophysical data are accessible for all drilled wells in a hydrocarbon field. In this paper, artificial neural network technology was used to estimate TOC from petrophysical logs. The correlation coefficient between the estimated and measured TOC data from Rock Eval pyrolysis is 71%, which is an acceptable value. The results of this study show that artificial intelligence is successful in estimating TOC data. Formation source rocks of the studied oilfield are Kazhdumi and Gadvan which constitute the main source rocks of Iran. The presented methodology is illustrated by using a case study from one well of Azadegan oil field in Abadan plain.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK, TOC, PETROPHYSICAL DATA, KAZHDUMI FORMATION, GADVAN FORMATION
 
References: 
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