International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064

Downloads: 143

Research Paper | Geophysics | Iraq | Volume 6 Issue 10, October 2017

3D Seismic Genetic Inversion for Reservoir Characterization and Prospects Identification

Ali M. Al-Rahim [2] | Ahmed Abdullah Abdulateef

Abstract: This study Examine the possibility of development of one of the fields in south Iraq. AL-Kumait oil field is conventional structural reservoir. In order to delineate the whole image of the structure and to explore for new prospect in this field. A 3D seismic survey is applied. Two formation considered in this survey we will focus on Nahr Umr formation in this study. Nahr Umr is a clastic reservoir contain 3 sand member identified by Wireline logging and core samples. Two wells drilled in this field KT_1 which is producing from the second sand member and KT_2 which contain only water. The picked horizon of the formation structurally interpreted. The aims of this study is to generate effective porosity cube based on inverted acoustic impedance cube and calculated effective porosity from well data to identify a good reservoir quality that can be considered as a new prospect. Genetic inversion of 3D seismic a patented Schlumberger product used as a method in petrel 2015 it uses a nonlinear multi trace operator to convert the seismic cube or its related attribute to a corresponding log property. A derived effective porosity from well logs of the two wells used to train the data and convert it to the porosity cube the result shows a good reservoir quality in two other closer and faulted area that can be a good new prospect.

Keywords: Reservoir characterization, Genetic inversion, Nahr Umr formation, 3D seismic

Edition: Volume 6 Issue 10, October 2017,

Pages: 488 - 493

How to Cite this Article?

Ali M. Al-Rahim, Ahmed Abdullah Abdulateef, "3D Seismic Genetic Inversion for Reservoir Characterization and Prospects Identification", International Journal of Science and Research (IJSR), Volume 6 Issue 10, October 2017, pp. 488-493,

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