Prediction of Total Organic Carbon (TOC) Utilizing ΔlogR and Artificial Neural Network (ANN) Methods and Geochemical Facies Determination of Kazhdumi Formation in One of the Fields - Southwest of Iran


1 Department of petroleum Geology, Research Institute of Petroleum Industry, Tehran, Iran

2 Petrophysicists, NIDC


Geochemical facies are intervals of source rock which have difference from upper and lower layers in terms of accumulation and production of organic matter. In this study two methods have been used for TOC determination of Kazhdumi Formation one of the fields of southwest of Iran. In the first method, TOC calculation has been done using experimental equation and ΔlogR method. In the next step, using the Artificial Neural Network (ANN) method in MATLAB software and using TOC information measured in the laboratory and well logging data as model input that have the highest correlation coefficient with TOC information, TOC log was estimated in the whole sequence of Kazhdumi Formation. The correlation coefficient of two TOC log estimated by ΔlogR and ANN methods with laboratory TOC data was 0.75 and 0.97, that shows more accuracy of ANN than ΔlogR method. Then, using MRGC (Multi-Resolution Graph-Based Clustering) method and using TOC logs obtained from ΔlogR and ANN methods as observers, the geochemical facies of the studied sequence were identified and the obtained model was generalized in several wells close to each other in the study field.
As a result, the thickness of suitable geochemical facies in terms of accumulation of organic matter are mostly seen in the initial intervals of the studied formation.


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