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Title: | 2D SEISMIC REFLECTION DATA INTERPRETATION INTEGRATED WITH RESERVOIR CHARACTERIZATION OF MIANO AREA USING SEISMIC AND WELL LOG DATA |
Authors: | Abdullah, Zubair |
Keywords: | Earth Sciences Geophysics |
Issue Date: | 2017 |
Publisher: | Quaid i Azam University |
Abstract: | Reservoir characterization using seismic and well data is a renowned technique within the content of hydrocarbon exploration. This study pertains to the interpretation of seismic lines, wireline logs and amplitude versus offset (AVO) modeling for improved characterization of reservoir level in Miano area, Lower Indus Basin, Pakistan. Geologically Miano area is found at the boundary of Central and Southern Indus Basin. This thesis work includes preparation of synthetic seismogram of Miano-09 well. Analysis of geophysical borehole logs provides one of the best approaches to characterizing rocks within boreholes. For the interpretation of the seismic lines, fours reflectors are marked by correlating synthetic seismogram on seismic section. As the area of study lies in the Lower Indus Basin, horst and graben geometry in this region is common which is confirmed by fault polygon and time and depth contours made from time and depth grid respectively. Petrophysics is the one of the most reliable tools for the confirmation of the types of the hydrocarbon and for marking of the proper zone of the interest of the presence of the hydrocarbon by combination of the different logs results. In this dissertation the petrophysics is performed on the Miano-09 well and zone of interest is marked at depth of 3331-3385m, which is the B-interval of Lower Goru sand formation. This zone contain 51.7% of water saturation and 47.2% of hydrocarbon saturation. At last the AVO modeling is performed in order to identify the class of the sand present in the reservoir zone. From AVO modeling it is confirmed that the B-interval sand of Lower Goru formation is class one sand. |
URI: | http://hdl.handle.net/123456789/15789 |
Appears in Collections: | BS |
Files in This Item:
File | Description | Size | Format | |
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EAR 1702.pdf | EAR 1702 | 3.9 MB | Adobe PDF | View/Open |
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