Paper Information

Journal:   GEOPERSIA   SUMMER-FALL 2017 , Volume 7 , Number 2; Page(s) 255 To 266.
 
Paper: 

ESTIMATION OF TOTAL ORGANIC CARBON FROM WELL LOGS AND SEISMIC SECTIONS VIA NEURAL NETWORK AND ANT COLONY OPTIMIZATION APPROACH: A CASE STUDY FROM THE MANSURI OIL FIELD, SW IRAN

 
 
Author(s):  ABDIZADEH HODA*, KADKHODAIE ALI, AHMADI ALI, HEIDARIFARD MOHAMMAD HOSEIN
 
* DEPARTMENT OF GEOLOGY, FACULTY OF SCIENCE, UNIVERSITY OF SISTAN AND BALUCHESTAN, ZAHEDAN, IRAN
 
Abstract: 

In this paper, 2D seismic data and petrophysical logs of the Pabdeh Formation from four wells of the Mansuri oilfield are utilized. DLog R method was used to generate a continuous TOC log from petrophysical data. The calculated TOC values by DLog R method, used for a multi-attribute seismic analysis.In this study, seismic inversion was performed based on neural networks algorithm and the resulting acoustic impedance was utilized as an important predictor attribute. Afterward, a probabilistic neural network was trained by using a set of predicting attributes derived from multiple regression. Subsequently, TOC was estimated by using seismic attributes with a correlation coefficient of 75%. In the next step of the study, the nonlinear ant colony optimization technique was utilized as an intelligent tool to generate a 2D TOC section from seismic attributes. Nonlinear ant colony optimization proposed an intelligently derived equation for which weight factors of each predictor seismic attribute in TOC estimation model were derived by using stochastic optimization. The results show that nonlinear ant colony equation (stochastic optimization) outperforms the probabilistic neural network model (gradient optimization).

 
Keyword(s): TOTAL ORGANIC CARBON, WELL LOGS, SEISMIC INVERSION, PROBABILISTIC NEURAL NETWORK, ANT COLONY OPTIMIZATION
 
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