Keywords and Phrases
Inversion; Neural Networks; Petrophysics; Reservoir Characterization; Seismic Attributes; Seismic Interpretation
"The Kapuni group within the Taranaki Basin in New Zealand is a potential petroleum reservoir. The objective of the study includes building a sequential approach to identify different geological features and facies sequences within the strata, through visualizing the targeted formations by interpreting and correlating the regional geological data, 3D seismic, and well data by following a sequential workflow. First, seismic interpretation is performed targeting the Kapuni group formations, mainly, the Mangahewa C-sand and Kaimiro D-sand. Synthetic seismograms and well ties are conducted for structural maps, horizon slices, isopach, and velocity maps. Well log and morphological analyses are performed for formation sequence and petrophysics identification. Attribute analyses including RMS, dip, azimuth, and eigenstructure coherence are implemented to identify discontinuities, unconformities, lithology, and bright spots. Algorithmic analyses are conducted using Python programming to generate and overlay the attributes which are displayed in 3D view. Integrating all of the attributes in a single 3D view significantly strengthens the summation of the outputs and enhances seismic interpretation. The attribute measurements are utilized to characterize the subsurface structure and depositional system such as fluvial dominated channels, point bars, and nearshore sandstone. The study follows a consecutive workflow that leads to several attribute maps for identifying potential prospects"--Abstract, page iv.
Liu, Kelly H.
Gao, Stephen S.
Anderson, Neil L. (Neil Lennart), 1954-
Guggenburger, Joe D., II
Geosciences and Geological and Petroleum Engineering
Ph. D. in Geology and Geophysics
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Up-scaled petrophysical analyses using micro-level field-of-view petrographic images for the Kapuni Group, Taranaki Basin, New Zealand
- Reservoir characterization using 3D seismic attributes and assisted petrophysical log analyses with artificial neural network
- Validation of poststack seismic inversion using rock physics analysis and 3D seismic and well correlation
xiv, 103 pages
© 2018 Aamer Ali AlHakeem, All rights reserved.
Dissertation - Open Access
Electronic OCLC #
AlHakeem, Aamer, "3D seismic attribute analysis and machine learning for reservoir characterization in Taranaki Basin, New Zealand" (2018). Doctoral Dissertations. 2659.