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Results
A review of some amplitude-based seismic geometric attributes and their applications
Verma, Sumit (University of Texas of the Permian Basin) | Chopra, Satinder (SamiGeo) | Ha, Thang (University of Oklahoma) | Li, Fangyu (Beijing University of Technology)
Abstract Seismic interpreters frequently use seismic geometric attributes, such as coherence, dip, curvature, and aberrancy for defining geologic features, including faults, channels, angular unconformities, etc. Some of the commonly used coherence attributes, such as cross correlation or energy-ratio similarity, are sensitive to only waveform shape changes, whereas the dip, curvature, and aberrancy attributes are based on changes in reflector dips. There is another category of seismic attributes, which includes attributes that are sensitive to amplitude values. Root-mean-square amplitude is one of the better-known amplitude-based attributes, whereas coherent energy, Sobel-filter similarity, normalized amplitude gradients, and amplitude curvature are among lesser-known amplitude-based attributes. We have computed not-so-common amplitude-based attributes on the Penobscot seismic survey from the Nova Scotia continental shelf consisting of the east coast of Canada, to bring out their interpretive value. We analyze seismic attributes at the level of the top of the Wyandot Formation that exhibits different geologic features, including a synthetic transfer zone with two primary faults and several secondary faults, polygonal faults associated with differential compaction, as well as fixtures related to basement-related faults. The application of the amplitude-based seismic attributes defines such features accurately. We take these applications forward by describing a situation in which some geologic features do not display any bending of reflectors but only exhibit changes in amplitude. One such example is the Cretaceous Cree Sand channels present in the same 3D seismic survey used for the previous applications. We compute amplitude curvature attributes and identify the channels, whereas these channels are not visible on the structural curvature display. In both of the applications, we observe that appropriate corendering not-so-common amplitude-based seismic attributes lead to convincing displays, which can be of immense aid in seismic interpretation and help define the different subsurface features with more clarity.
- North America > Canada > Nova Scotia (0.35)
- North America > United States > Oklahoma (0.28)
- North America > United States > Texas (0.28)
- Geology > Structural Geology (0.93)
- Geology > Geological Subdiscipline > Stratigraphy > Lithostratigraphy (0.54)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (26 more...)
Unsupervised machine learning facies classification in the Delaware Basin and its comparison with supervised Bayesian facies classification
Chopra, Satinder (TGS, Calgary) | Marfurt, Kurt (University of Oklahoma) | Sharma, Ritesh (TGS)
ABSTRACT An ongoing challenge to seismic interpreters is to identify and extract heterogeneous seismic facies on data volumes that are continually increasing in size. Geometric, geomechanical, and spectral attributes help to extract key features but add to the number of data volumes to be examined. Common analysis tools include interactive co-rendering, crossplotting, and 3D visualization where we examine more than one attribute at a time, data reduction, where we mathematically reduce the number of data volumes to a more manageable subset, clustering, where the goal is to identify voxels that have similar expressions, and supervised classification, where the computer attempts to mimic the skills of an experienced interpreter. In this study we compare several of the more well established machine learning techniques: waveform classification, principal component analysis (PCA), k-means clustering, and supervised Bayesian classification to a seismic data volume from the Delaware Basin. We also examine some less common clustering techniques applied to seismic attributes including independent component analysis (ICA), self-organizing mapping and generative topographic mapping. We find that the machine learning methods hold promise as each of them exhibits more vertical and spatial resolution than the waveform classification, or the supervised Bayesian classification. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: Poster Station 3 Presentation Type: Poster
- North America > United States > Texas (1.00)
- North America > United States > New Mexico (0.87)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline (1.00)
- Oceania > New Zealand > North Island > Taranaki Basin (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- North America > United States > Texas > Permian Basin > Delaware Basin > Wolfcamp Shale Formation (0.99)
- (7 more...)
Introduction to special section: Shale oil and gas enrichment mechanisms and effective development: Concepts, methodologies, and case studies
Gao, Dengliang (West Virginia University) | Jin, Zhijun (SINOPEC, SINOPEC) | Duan, Taizhong (SINOPEC, SINOPEC) | Zeng, Hongliu (The University of Texas at Austin) | Chopra, Satinder (TGS) | Carr, Tim (West Virginia University) | Marfurt, Kurt (University of Oklahoma) | Rich, Jamie (Devon Energy Corporation)
- North America > United States > West Virginia (0.52)
- North America > United States > Texas (0.47)
- North America > Canada > Alberta (0.47)
- (3 more...)
- Geology > Structural Geology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- North America > United States > West Virginia > Appalachian Basin (0.99)
- North America > United States > Virginia > Appalachian Basin (0.99)
- North America > United States > Tennessee > Appalachian Basin (0.99)
- (9 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (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)
- (2 more...)
Coherence attribute applications on seismic data in various guises
Chopra, Satinder (Arcis Seismic Solutions, TGS) | Marfurt, Kurt (University of Oklahoma)
ABSTRACT The iconic coherence attribute is very useful for geologic feature imaging such as faults, deltas, submarine canyons, karst collapse, mass transport complexes, and more. Besides its preconditioning, the interpretation of discrete stratigraphic features on seismic data is also limited by its bandwidth, where in general the data with higher bandwidth yields crisper features than data with lower bandwidth. Some form of spectral balancing applied to the seismic amplitude data can help in achieving such an objective, so that coherence run on spectrally balanced seismic data yields a better definition of the geologic features of interest. The quality of the generated coherence attribute is also dependent in part on the algorithm employed for its computation. In the eigenstructure decomposition procedure for coherence computation, spectral balancing equalizes each contribution to the covariance matrix, and thus yields crisper features on coherence displays. There are other ways to in addition to simple spectral balancing, including the amplitude-volume technique, taking the derivative of the input amplitude, spectral bluing, and thin-bed spectral inversion. We compare some of those techniques, and show their added value in seismic interpretation. Presentation Date: Monday, September 25, 2017 Start Time: 3:05 PM Location: Exhibit Hall C/D Presentation Type: POSTER
- Geology > Rock Type > Sedimentary Rock (0.75)
- Geology > Geological Subdiscipline > Stratigraphy (0.68)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.90)
Understanding the seismic disorder attribute and its applications
Chopra, Satinder (TGS) | Marfurt, Kurt (University of Oklahoma)
ABSTRACT While reflections associated with conformal sedimentary layers are usually coherent and continuous, other reflections such as mass transport complexes, karst collapse, and salt, may appear to be quite chaotic, without any specific orientation. We may also see chaotic events that have little to do with the target geology, but rather are artifacts due to variations in the overburden and surface or budget limitations resulting in a suboptimum acquisition program. While some of these artifact issues can be handled at the time of processing, a certain level of randomness remains in most seismic data volumes. Geologic features of interpretational interest such as fault damage zones, unconformities, and gas chimneys often have randomness associated with them, which can be characterized in terms of seismic disorder attribute amongst others. We demonstrate the application of seismic disorder attribute to two different datasets and find that it is a useful attribute for assessing the signal-to-noise ratio and data quality, in addition to helping delineate damage zones associated with large faults, and the interior of salt dome structures. Presentation Date: Monday, October 17, 2016 Start Time: 2:15:00 PM Location: Lobby D/C Presentation Type: POSTER
- North America > United States (0.29)
- North America > Canada > Alberta (0.16)
- Geology > Structural Geology (1.00)
- Geology > Rock Type > Sedimentary Rock (0.90)
- Geology > Geological Subdiscipline > Stratigraphy (0.68)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.89)
Coherence and curvature attributes on preconditioned seismic data
Chopra, Satinder (University of Oklahoma) | Misra, Somanath (University of Oklahoma) | Marfurt, Kurt J. (University of Oklahoma)
Seismic data are usually contaminated with both random and coherent noise, even when the data have been properly migrated and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or postmigration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team.
Seismic Attributes On Frequency-enhanced Seismic Data
Chopra, Satinder (Arcis Corporation) | Misra, Somanath (Arcis Corporation) | Marfurt, Kurt J. (University of Oklahoma)
Summary Seismic data are usually contaminated by both random and coherent noise, even when the data have been migrated reasonably well and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structureoriented filtering or post migration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team to alleviate those problems. Another common problem with seismic data is their relatively low bandwidth. Significant efforts are made during processing to enhance the frequency content of the data as much as possible to provide a spectral response that is consistent with the acquisition parameters. Ironically, the interpreters can be somewhat more aggressive in their filtering. The interpreters will have a better understanding of the geology, the play concept, access to any well data, and therefore be better able to keep or reject alternative filter products that are consistent or inconsistent with the interpretation hypothesis. We begin our discussion by reviewing alternative means of suppressing random noise on our migrated seismic images, with the most promising methods being various implementations of structure-oriented filtering. Next, we address acquisition footprint, which may appear to be random in the temporal domain but is highly correlated to the acquisition geometry in the spatial domain. After running the data through the cleaning phase, we evaluate alternative methods for frequency enhancement of the input seismic data. We illustrate the impact of these preconditioning steps on the computation of the attributes such as coherence and curvature on data volumes from Alberta, Canada. We conclude with a summary on the choice of the frequency-enhancement methods on the basis of the examples generated with different workflows. Introduction โ Alternative noise-suppression workflows Suppression of random noise: Mean, alpha-trimmed mean, and median filters are commonly used during processing to suppress random noise. A more desirable application would be of a dip-steered mean or median filter, which has the effect of enhancing laterally continuous events by reducing randomly distributed noise without suppressing details in the reflection events consistent with the structure. The filter picks up samples within the chosen aperture along the local dip and azimuth and replaces the amplitude of the central sample position with the median value of the amplitudes. The median filter can also be applied iteratively, reducing random noise at each successive iteration, but will not significantly increase the high frequency geological component of the surface (Chopra and Marfurt, 2008). Dip-steered mean filters work well on prestack data in which discontinuities appear as smooth diffractions, but smear faults and stratigraphic edges on migrated data. Dipsteered median and alpha-trimmed mean filters work somewhat better but will still smear faults. Hoecker and Fehmers (2002) address this problem through an โanisotropic diffusionโ smoothing algorithm. The anisotropic part is so named because the smoothing takes place parallel to the reflector, while no smoothing takes place perpendicular to the reflector.
Integration of coherence and volumetric curvature images
Chopra, Satinder (University of Oklahoma) | Marfurt, Kurt J. (University of Oklahoma)
Volumetric attributes computed from 3D seismic data are powerful tools in the prediction of fractures and other stratigraphic features. Geologic structures often exhibit curvature of different wavelengths, providing different perspectives of the same geology. Tight (short-wavelength) curvature delineates details within intense, highly localized fracture systems. Broad (long-wavelength) curvature usually enhances subtle flexures on the scale of 100โ200 traces that are difficult to see in conventional seismic, but often correlate to fracture zones below seismic resolution, and also collapse features and diagenetic alterations that result in broader bowls. We present a number of curvature examples demonstrating their interpretational value.
- Geology > Structural Geology > Tectonics (0.89)
- Geology > Geological Subdiscipline > Stratigraphy (0.68)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.70)
- North America > United States > Texas > Fort Worth Basin (0.99)
- Oceania > Australia > Victoria > Bass Strait > Gippsland Basin (0.98)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.98)
- (48 more...)
Emerging and future trends in seismic attributes
Chopra, Satinder (University of Oklahoma) | Marfurt, Kurt J. (University of Oklahoma)
Seismic attributes extract information from seismic reflection data that can be used for quantitative and qualitative interpretation. Attributes are used by geologists, geophysicists, and petrophysicists to map features from basin to reservoir scale. Some attributes, such as seismic amplitude, envelope, rms amplitude, spectral magnitude, acoustic impedance, elastic impedance, and AVO are directly sensitive to changes in seismic impedance. Other attributes such as peak-to-trough thickness, peak frequency, and bandwidth are sensitive to layer thicknesses. Both classes of attributes can be quantitatively correlated to well control using multivariate analysis, geostatistics, or neural networks. Seismic attributes such as coherence, Sobel filter-based edge detectors, amplitude gradients, dip-azimuth, curvature, and gray-level co-occurrence matrix measures are directly sensitive to seismic textures and morphology. Geologic models of deposition and structural deformation coupled with seismic stratigraphy principles and seismic geomorphology allow us to qualitatively predict geologic facies.
- North America > Canada > Alberta (0.46)
- North America > United States > Wyoming (0.28)
- Geology > Sedimentary Geology > Depositional Environment (1.00)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Texas > Fort Worth Basin (0.99)
- North America > United States > Montana > Powder River Basin (0.99)