Evaluation of Organic Matter Content Achieved from Artificial Neural Network in a Sequence Stratigraphic Framework: A Case Study from Pabdeh Formation of Marun Oilfield

Authors

1 Associate Professor, Department of Geology, Petroleum Geology and Geochemistry Research Centre (PGGRC), Shahid Chamran University Of Ahvaz

2 M.Sc., Department of Geology, Shahid Chamran University Of Ahvaz

3 M.Sc., National Iranian South Oil Company

Abstract

Evaluation of geochemical characters in a sequence stratigraphic framework, along with increase in interprets accuracy, reveals the effects of change in environmental condition on these characters. In this study, modeling a three-layered back-propagation network which has about 89% total precision was the subsequent of using Artificial Neural Network technique in order to assess the Total Organic Carbon (TOC) from petrophysical data. Sequence stratigraphy study demonstrated that the Pabdeh Formation of the Marun oilfield (Middle Eocene to Early Oligocene) has experienced several transgression and regression in its depositional span, causes different environmental conditions with various richness of organic matter in their sediments. Hereby, TOC content varies from 0.45 to 4 wt.%. This research work proposes a good agreement among petrophysical, geochemical and sequence stratigraphic boundaries. Also, results reveal that the best environmental condition existed in the Late Eocene whose domination is due to increase in sea level and subsequently high organic matter entrance and creation suitable reduction condition to preserve these materials.
 

Keywords

References

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