scholarly journals Synthetic seismograms from borehole seismic data and well logs, Beaufort-Mackenzie Basin

2015 ◽  
Author(s):  
K Hu ◽  
T A Brent ◽  
D R Issler ◽  
Z Chen
2021 ◽  
pp. 3942-3951
Author(s):  
Ali K. Jaheed ◽  
Hussein H. Karim

The Amarah Oil field structure was studied and interpreted by using 2-D seismic data obtained from the Oil  Exploration company. The study is concerned with Maysan Group Formation (Kirkuk Group) which is located in southeastern Iraq and belongs to the Tertiary Age. Two reflectors were detected based on synthetic seismograms and well logs (top and bottom Missan Group). Structural maps were derived from seismic reflection interpretations to obtain the location and direction of the sedimentary basin. Two-way time and depth maps were conducted depending on the structural interpretation of the picked reflectors to show several structural features. These included three types of closures, namely two anticlines extended in the directions of S-SW and NE, one nose structure (anticline) in the middle of the study area,  and structural faults in the northeastern part of the area, which is consistent with the general fault pattern. The seismic interpretation showed the presence of some stratigraphic features. Stratigraphic trap at the eastern part of the field, along with other phenomena, such as flatspot (mound), lenses, onlap, and toplap, were detected as indications of potential hydrocarbon accumulation in the region.


2009 ◽  
Author(s):  
Teck Kean Lim ◽  
Aqil Ahmed ◽  
Muhammad Antonia Gibrata ◽  
Gunawan Taslim

2021 ◽  
pp. 1-50
Author(s):  
Yongchae Cho

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Since natural fractures are highly heterogeneous and sub-seismic scale, integrating petrophysical data (i.e., cores, well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I introduce an integrated and stochastic approach for discrete fracture network modeling with field data demonstration. In the proposed method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well log data which includes localized but high-confidence information. By using the fracture intensity model including both seismic and well logs, I build the final natural fracture model which can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, the proposed workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well log data.


2012 ◽  
Vol 03 (04) ◽  
pp. 872-877 ◽  
Author(s):  
Godwin Emujakporue ◽  
Cyril Nwankwo ◽  
Leonard Nwosu
Keyword(s):  

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. S135-S150
Author(s):  
Jakob B. U. Haldorsen ◽  
Leif Jahren

We have determined how the measured polarization and traveltime for P- and S-waves can be used directly with vertical seismic profile data for estimating the salt exit points in a salt-proximity survey. As with interferometry, the processes described use only local velocities. For the data analyzed in this paper, our procedures have confirmed the location, inferred from surface-seismic data, of the flank of a steeply dipping salt body near the well. This has provided us more confidence in the estimated reservoir extent moving toward the salt face, which in turn has added critical information for the economic evaluation of a possible new well into the reservoir. We also have found that ray-based vector migration, based on the assumptions of locally plane wavefronts and locally plane formation interfaces, can be used to create 3D reflection images of steeply dipping sediments near the well, again using only local velocities. Our local reflection images have helped confirm the dips of the sediments between the well and the salt flank. Because all parameters used in these processes are local and can be extracted from the data themselves, the processes can be considered to be self-sufficient.


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