Download Horizon Eri Sugai
Sixta Strissel
The Intensive English and Leadership Development Program is a 17-day summer program sponsored by the Sugai Fund. This program seeks to give students the opportunity to broaden their horizons through educational and cultural experiences at GW.
(A) Location of sampling sites within the Ogasawara (Bonin) Islands, in the northwestern Pacific Ocean. O-horizon and surface mineral soil samples were collected from three sites: YAK1, YAK2, and HHI1. (B) Relative abundance of soil bacterial communities at the phylum level. (C) Relative abundance of soil fungal communities at the phylum level.
Today, with the development of 3-D studies and the increase in seismic data volume, there is a growing need to expand interpretation techniques for achieving higher speed and accuracy of interpretation tasks. Determining seismic faults and horizons is vital to accomplish the process as one of the essential stages of data interpretation. With the recent development of computational methods in seismic interpretation and their benefits, different approaches have been promoted. The specialist can make the understanding much faster with higher accuracy. In this research, a fully automated dual horizon and fault selection approach in the presence of semi-vertical faults is presented using a structural smoothing condition. Geological faults make it challenging to map sedimentary layers appropriately which is targeted for review in this work. Unlike Image processing techniques that determine the location of faults only, the proposed approach gives the benefit of the estimated fault displacement. In this method, faults are modeled as a displacement vector field. Despite traditional methods (such as similarity and coherence), in this method, the vector field of the estimated fault displacement determines the displacement and its location. This vector field can be used for auto-determination of fault-related layers displacement. As a result, automatic horizon picking in the presence of such faults is possible, thereby simplifying the mapping of sedimentary layers.
Geological modeling, seismic properties, and petrophysical modeling using logs, interval velocities, cores, and 3D seismic data are utilized to create structural elements on seismic data for reservoir modeling (Anees et al. 2022a, b). In the case of automatic horizon and fault picking, knowing about seismic objects is essential. For example, fluvial paleo-channels can be detected easily by logs data, core, and outcrop. Still, due to the outreach of several faults and limited seismic separability in the study region, it is difficult to recognize them by conventional seismic methods(Anees et al. 2019).
A study was conducted in the Hangjinqi region in China. The base target of this research is to identify the horizontal geometry of the numerous channels. Based on the study of unified analyses of geological cores, geophysical well-logs, and seismic properties, it is confirmed that multiple channels are available in the study area. Furthermore, channel dispensation based on seismic properties and its overlapping on the paleogeographic map showed that they are coming from the North and are merging in the South direction (Anees et al. 2019).
In 2022 a study was conducted on sweet spots prediction through fracture genesis. This research engaged structural smoothing and data analysis using directional filtering to sharpen the structural discontinuities as well as 3D visualization and automatic tracking were conducted to expound on the faults and horizons (Jiang et al. 2022). In 2022, a study was conducted in the Northern Basin of China to identify gas accumulation in appropriate areas using faults and sedimentary facies. This study is conducted by incorporating the 3D seismic grid, well logs, and several cores using seismic stratigraphy, geological modeling, seismic attribute analysis, and well logging to identify gas accumulation zones. The unified results showed that the North-Western sector was uplifted compared to the southern sector. The natural gas accumulated in the southern region was migrated through fault into the northern zone and showed that the favorable zones of gas accumulation lie toward the northern region (Anees et al. 2022a, b).
This study sought to replace the automated process using the structural smoothing method with the manual method to show the displacement faults to determine the seismic horizons in the presence of a fault. In this process, fault curves are extracted from two-dimensional images of a mixed seismic attribute (mapped instantaneous phase energy abbreviated as MIPE). The displacements resulting from faults around these curves are obtained. Theoretically, the automatic determination of seismic horizons was thoroughly investigated using the structural smoothing method. It was tested in computer programs using various artificial and accurate data. This method fully includes ideal conditions, assuming no random and non-random noise, turbulent zone conditions, convergent horizons in the presence of a strong reflector, and the use of other seismic indicators. Matlab software is used to develop computer codes.
In continuation, the methods and applications are presented. Then, the mathematics of the method, which includes fault modeling, filtering, shearing, and determination of structural tensors, was discussed. In the following, the automatic determination of seismic horizons in the presence of semi-vertical faults was discussed using the structural smoothing method in the MIPE section. The necessary processes for determining the position of the fault and then selecting the seismic horizon before and after its location were discussed in detail. Finally, the horizon and faults are imported to Opendtect.
Structural smoothing coefficients are extracted from the seismic/attribute input image due to the related structures. Smoothing along structures apparent in seismic images can enhance these structural features while preserving significant discontinuities such as faults or channels. (Ashraf et al. 2019). The selected directions are obtained using the scan estimates (over a group of input directions) and choosing the most similar ones or among those whose comparison markers are maximal. Directional scans can provide the similarities and hints needed for structural smoothing. Searching for the optimal direction before scanning is costly if the information is unavailable on the linearity or plane of the imaged structures. Buried channels are the properties of curved lines in seismic images. In comparison, geological horizons appear as curved planes. Both structural smoothing and similarity methods are expected to consider the different dimensions of apparent structures in seismic images (Jiang et al. 2022). Therefore, directional and dimensional scanning is computationally expensive for 3D images.
The Fault position assessment is by two-stage pattern analysis. The first step in the study is to estimate the fault position. The next step is to remove the fault from the fault zone with one-pixel precision. In seismic images, the vertical displacements between sequences, before and after cutting, are zero in horizontal geological layers. Therefore, the cut can change the removal between the sequences when the layers are sloping. As shown in Fig. 2b, \(f\) and \(f^\prime\) are the pre-and post-cut images.
where ρ' is a function of the shear value of s, the slopes \(\rho\) and \(\rho^\prime\) are vertical displacements, precisely at the selected point, before and after cutting. When \(\rho^\prime\left( s \right) = \rho = 0\) and \( \rho = 0\), which means that the cut will not affect the horizontal layer The innovation in our methodology is using a seismic attribute that is the map of the instantaneous phase in the energy section. Mixed instantaneous phase energy (MIPE) is simply a multiplication of normalized instantaneous phase and energy attributes that highlights both the significance of amplitudes and phase characteristics of seismic reflected waveshape simultaneously.
Figure 3 shows the different functional relationship patterns where the apex of the black curve in Fig. 3b corresponds to the black pixels in Fig. 3a, and this pixel is located precisely on the fault. Figure 3b shows the horizontal coordinates of the peak point \( l_2\), which cuts the image vertically (with a ratio of l \(\fracl_2 l_1 \)). The curves in Fig. 3c correspond to the relationships shown in the equation.
These vectors should be zero or close to zero in areas with no faults. Near-horizontal faults are not easily discernible even by commentators. Therefore, the faults considered in this study are more vertical, and it is assumed that the angle between the fault curve and the vertical line is less than 45
The shear wave reflectivity of the fault zone is taken from one-dimensional normal occurrence modeling of accurate geologic fault zone models. The shear wave reflectivity of the fault zone, which originates from fine compositional layering, is moderate and can be directly associated with singular geologic formations. Within the fault zone, variable shear wave reflectivity, both horizontally and vertically, originates from geologic dissimilarity(McCaffree and Christensen 1993). Seismic anisotropy, especially shear wave splitting, provides strong evidence for coherent deformation over several tens of km wide in the lithospheric mantle under major transcurrent faults. Yet it cannot find narrow strain localization zones or shallowly dipping faults (Vauchez et al. 2012).
Combining the structural smoothing method on the MIPE section with seismic markers to determine the horizon and other geological and stratigraphic features is done. Determination of seismic horizon and geological and stratigraphic characteristics of the F3 block located in the North Sea, using Opendtect software and structural smoothing program, is targeted. Data were analyzed using seismic software indicators. To Identify the functionality of the proposed workflow inline No. 250 is selected (Fig. 6). The abnormal high values are even evident clearly on mixed instantenous phase energy (MIPE) section (Fig. 7).
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