scholarly journals Seismic Attributes Method for Prediction of Unconsolidated Sand Reservoirs of Heavy Oil

2015 ◽  
Vol 8 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Lei Zhang ◽  
Donghui Zhu ◽  
Xuejuan Zhang

Heavy crude oil is known as oil that is highly viscous and of a higher density than that of conventional oil. Sand reservoirs containing heavy oil generally consist of unconsolidated sediments deposited at a shallow burial depth, with high porosity and permeability. In seismic exploration, acoustic impedance inversion is a commonly used tool in reservoir prediction. However, due to the unconsolidated characteristic of heavy oil reservoirs, the wave impedance difference between heavy oil sandstones and mudstones becomes less apparent, thus limiting the ability of impedance inversion to accurately characterize the reservoir. Therefore we must expand our characterization of the target heavy oil reservoirs to include correlation analysis of different seismic attributes to the unconsolidated reservoir thickness. The results show that there has a strong correlation between the seismic attribute value of instantaneous frequency and unconsolidated reservoir thickness, more than other seismic attributes in the target strata. Thus the instantaneous frequency attribute can be used to predict qualitatively the lateral distribution of unconsolidated reservoirs, which in turn, indicates the vertical variation of thickness for the unconsolidated reservoirs. By using frequency attributes which are sensitive to unconsolidated sediments, coupling with additional geologic information, we can predict the distribution of sedimentary facies accurately in the study area, which results in a more reliable prediction for the lateral and vertical distributions of heavy oil reservoirs.

2020 ◽  
Vol 12 (1) ◽  
pp. 1158-1168
Author(s):  
Chris Adesola Samakinde ◽  
Jan Marinus van Bever Donker ◽  
Oluwaseun Adejuwon Fadipe

AbstractThe reported occurrence of Albian- and Cenomanian-aged braided fluvio-deltaic channels in the Orange Basin, South Africa, opens a window of exploration activities to characterize these channels as they are renowned to form some of the world’s giant oil field. In this study, a seismic acoustic impedance inversion and seismic attributes (instantaneous frequency and iso-frequency) analysis is used to investigate potential Albian and Cenomanian fluvio-deltaic channels in offshore, northern Orange Basin. Reservoirs were mapped using a well and 3D seismic volume (8-bit) after initial dip-steering coherency filtering had been performed on the seismic volume to remove incoherent noise and improve data resolution. Model-based acoustic impedance inversion was applied on the seismic volume to delineate fluvio-deltaic channels in addition to using the RMS (root mean square) amplitude attribute. Iso-frequency using the cosine correlative transform (CCT) method was equally applied to delineate these channels. Instantaneous frequency attribute was analyzed for potential hydrocarbon-charged sediments. This was achieved by utilizing thirty-three seismic traces as an input in the Hilbert transform window, after which trace envelope and instantaneous phase were transformed into instantaneous frequency. Acoustic impedance inversion results reveal the presence of two channels within the Cenomanian sequence, which shows high porosity (∼40%) along its geometry. The CCT method shows that the 8 Hz frequency window resolved the presence of a channel within the Albian sequence. A meandering channel within the Albian sequence was equally delineated by the RMS, while the application of instantaneous frequency (IF) attribute indicates the presence of hydrocarbon-charged sediments of Cenomanian age in proximity to a listric normal fault because of the attenuation of frequency observed close to the fault. This study demonstrates a case study of the application of seismic impedance inversion and seismic attributes for the delineation of potential reservoirs and hydrocarbon-charged sediments in a basin.


2021 ◽  
Vol 201 ◽  
pp. 108436
Author(s):  
Daode Hua ◽  
Pengcheng Liu ◽  
Peng Liu ◽  
Changfeng Xi ◽  
Shengfei Zhang ◽  
...  

2016 ◽  
Author(s):  
Cenk Temizel ◽  
Aera Energy ◽  
Karthik Balaji ◽  
Rahul Ranjith ◽  
Chris Coman

2015 ◽  
Author(s):  
Lei Wang ◽  
Huiqing Liu ◽  
Zhanxi Pang ◽  
Xueqi Cen ◽  
Jing Xia ◽  
...  

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