Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Results
ABSTRACT Prediction of shear-wave velocity plays an important role in some seismic applications, such as amplitude variation with offset (AVO) analysis, and the construction of lithofacies recognition library. This paper presents a method for predicting S-wave velocity based on three logging curves: interval transit time, density and gamma. There are two main problems: accurate establishment of reasonable rock physical models and efficient solution of an objective function. Three logging curves can be used to calculate porosity, shale volume and to determine the lithology. We can get the compaction constant through solving the P-wave objective function. An advantage of this method is that there are few empirical parameters, such as clay content, mineral modulus and pore aspect ratio, to be chosen. On the other hand, it can accurately predict S-wave velocity by building a reasonable rock physical models with practical well logs rather than petrophysical experiment in the laboratory. Presentation Date: Tuesday, September 26, 2017 Start Time: 3:30 PM Location: Exhibit Hall C/D Presentation Type: POSTER
Prestack multiwave joint inversion for Young's modulus and Poisson ratio based on stochastic kriging interpolation
Yu, Bo (China University of Petroleum–Beijing) | Zhou, Hui (China University of Petroleum–Beijing) | Zou, Xiaofeng (China University of Petroleum–Beijing) | Zu, Shaohuan (China University of Petroleum–Beijing) | Wang, Ning (China University of Petroleum–Beijing) | Wang, Shucheng (China University of Petroleum–Beijing)
ABSTRACT The product of Young's modulus and density can highlight abnormal characteristic of shale gas reservoirs, Poisson ratio can indicate fluid property. In this paper, the joint PP and PS AVO inversion based on Bayes theorem is used to obtain Young's modulus and Poisson's ratio. This method can achieve more accurate elastic parameters for fluid prediction and shale gas reservoir identification. We get the PP and PS wave approximate reflection coefficient by the approximate equation of Aki-Richards. We obtain the object function by Bayes theorem, and we suppose the parameter sequence is subject to the Cauchy distribution. To reduce the influence of initial model, we use well log data to obtain a reasonable prior model by stochastic kriging interpolation. Presentation Date: Wednesday, October 19, 2016 Start Time: 4:00:00 PM Location: 162/164 Presentation Type: ORAL
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.46)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
The vertical radar profiling VRP technique is able to explore much deeper than conventional surface groundpenetrating radar GPR because it uses boreholes. It has been successfully applied at the Sendai Castle site in Japan to investigate the extent of an old stone wall and strata buried by a more recent stone wall. The transmitter of a polarimetric radar system was moved within a borehole, and the receiver was fixed on the ground surface several meters away from the borehole head. Cross and copolarization data were measured at a receiver position with a different orientation to the receiver. Ten data sets were acquired by placing the receiver in five directions. The depolarization is strong, indicating the subsurface contains a great amount of gravel. To get clear and intuitive images of the subsurface, we applied data processing techniques, including the separation of direct and reflected waves of raw VRP data using f-k filtering approach and Kirchhoff migration of separated reflected waves. By comparing the migrated images, we learned that cross and copolarization data sets received at the same position give the same images of the subsurface, although the appearances of the original data sets are different. The degree of consistency of all data sets recorded in different directions is quite high, and the migrated images near the borehole fit the borehole core very well. The images reveal the distribution of the old stone wall and other layers.