<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Due to the high exploration cost, limited number of wells for source rocks drilling and scarce test samples for the Total Organic Carbon Content (TOC) in the Huizhou sag, the TOC prediction of source rocks in this area and the assessment of resource potentials of the basin are faced with great challenges. In the study of TOC prediction, predecessors usually adopted the logging assessment method, since the data is only confined to a “point” and the regional prediction of the source bed in the seismic profile largely depends on the recognition of seismic facies, making it difficult to quantify TOC. In this study, we combined source rock geological characteristics, logging and seismic response and built the mathematical relation between quasi TOC curve and seismic data based on the TOC logging date of a single well and its internal seismic attribute. The result suggested that it was not purely a linear relationship that was adhered to by predecessors, but was shown as a complicated non-linear relationship. Therefore, the neural network algorithm and SVMs were introduced to obtain the optimum relationship between the quasi TOC curve and the seismic attribute. Then the goal of TOC prediction can be realized with the method of seismic inversion.</p></abstract>

Due to the high exploration cost, limited number of wells for source rocks drilling and scarce test samples for the Total Organic Carbon Content (TOC) in the Huizhou sag, the TOC prediction of source rocks in this area and the assessment of resource potentials of the basin are faced with great challenges. In the study of TOC prediction, predecessors usually adopted the logging assessment method, since the data is only confined to a “point” and the regional prediction of the source bed in the seismic profile largely depends on the recognition of seismic facies, making it difficult to quantify TOC. In this study, we combined source rock geological characteristics, logging and seismic response and built the mathematical relation between quasi TOC curve and seismic data based on the TOC logging date of a single well and its internal seismic attribute. The result suggested that it was not purely a linear relationship that was adhered to by predecessors, but was shown as a complicated non-linear relationship. Therefore, the neural network algorithm and SVMs were introduced to obtain the optimum relationship between the quasi TOC curve and the seismic attribute. Then the goal of TOC prediction can be realized with the method of seismic inversion.

Geophysical Prediction Technology Based on Organic Carbon Content in Source Rocks of the Huizhou Sag, the South China Sea

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Due to the high exploration cost, limited number of wells for source rocks drilling and scarce test samples for the Total Organic Carbon Content (TOC) in the...

Geophysical Prediction Technology Based on Organic Carbon Content in Source Rocks of the Huizhou Sag, the South China Sea

Published Online: Sep 13, 2017

Page range: 4 - 13

DOI: https://doi.org/10.1515/pomr-2017-0058

Keywords
Huizhou sag, Source rock, TOC, Separate-frequency inversion, Geophysical prediction

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Geophysical Prediction Technology Based on Organic Carbon Content in Source Rocks of the Huizhou Sag, the South China Sea

Published Online: Sep 13, 2017

Page range: 4 - 13

DOI: https://doi.org/10.1515/pomr-2017-0058

KeywordsHuizhou sag, Source rock, TOC, Separate-frequency inversion, Geophysical prediction

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Huizhou sag, Source rock, TOC, Separate-frequency inversion, Geophysical prediction