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Journal articles on the topic '3D seismic interpretation'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Roberts, D. G. "3D Seismic Interpretation." Marine and Petroleum Geology 21, no. 3 (2004): 422. http://dx.doi.org/10.1016/j.marpetgeo.2004.03.001.

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Brown, Alistair R. "Pitfalls in 3D seismic interpretation." Leading Edge 24, no. 7 (2005): 716–17. http://dx.doi.org/10.1190/1.1993265.

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Dirstein, James K., and Gary N. Fallon. "Automated interpretation of 3D seismic." Preview 2011, no. 151 (2011): 30–37. http://dx.doi.org/10.1071/pvv2011n151p30.

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Halpert, Adam D., Robert G. Clapp, and Biondo Biondi. "Salt delineation via interpreter-guided 3D seismic image segmentation." Interpretation 2, no. 2 (2014): T79—T88. http://dx.doi.org/10.1190/int-2013-0159.1.

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Although it is a crucial component of seismic velocity model building, salt delineation is often a major bottleneck in the interpretation workflow. Automatic methods like image segmentation can help to alleviate this bottleneck, but issues with accuracy and efficiency can hinder their effectiveness. However, a new graph-based segmentation algorithm can, after modifications to account for the unique nature of seismic data, quickly and accurately delineate salt bodies on 3D seismic images. In areas where salt boundaries are poorly imaged, limited manual interpretations can be used to guide the a

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Gao, Dengliang. "Volume texture extraction for 3D seismic visualization and interpretation." GEOPHYSICS 68, no. 4 (2003): 1294–302. http://dx.doi.org/10.1190/1.1598122.

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Visual inspection of poststack seismic image patterns is effective in recognizing large‐scale seismic features; however, it is not effective in extracting quantitative information to visualize, detect, and map seismic features in an automatic and objective manner. Although conventional seismic attributes have significantly enhanced interpreters' ability to quantify seismic visualization and interpretation, very few attributes are published to characterize both intratrace and intertrace relationships of amplitudes from a three‐dimensional (3D) perspective. These relationships are fundamental to

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Di, Haibin, Cen Li, Stewart Smith, Zhun Li, and Aria Abubakar. "Imposing interpretational constraints on a seismic interpretation convolutional neural network." GEOPHYSICS 86, no. 3 (2021): IM63—IM71. http://dx.doi.org/10.1190/geo2020-0449.1.

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With the expanding size of 3D seismic data, manual seismic interpretation becomes time-consuming and labor-intensive. For automating this process, recent progress in machine learning, in particular the convolutional neural network (CNN), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation aims at mimicking the intelligence of experienced seismic interpreters to annotate subsurface geology accurately and efficiently. However, most of the implementations and applications are rela

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Ali, Kamal. "3D SEISMIC ATTRIBUTES INTERPRETATION OF ZUBAIR FORMATION IN AL-AKHAIDEIR AREA, SOUTHWESTERN KARBALA." Iraqi Geological Journal 53, no. 1D (2020): 17–25. http://dx.doi.org/10.46717/igj.53.1d.2rw-2020-05-01.

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8

Wrona, Thilo, Indranil Pan, Rebecca E. Bell, Robert L. Gawthorpe, Haakon Fossen, and Sascha Brune. "3D seismic interpretation with deep learning: A brief introduction." Leading Edge 40, no. 7 (2021): 524–32. http://dx.doi.org/10.1190/tle40070524.1.

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Understanding the internal structure of our planet is a fundamental goal of the earth sciences. As direct observations are restricted to surface outcrops and borehole cores, we rely on geophysical data to study the earth's interior. In particular, seismic reflection data showing acoustic images of the subsurface provide us with critical insights into sedimentary, tectonic, and magmatic systems. However, interpretations of these large 2D grids or 3D seismic volumes are time-consuming, even for a well-trained person or team. Here, we demonstrate how to automate and accelerate the analysis of the

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Paumard, Victorien, Julien Bourget, Benjamin Durot, et al. "Full-volume 3D seismic interpretation methods: A new step towards high-resolution seismic stratigraphy." Interpretation 7, no. 3 (2019): B33—B47. http://dx.doi.org/10.1190/int-2018-0184.1.

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Following decades of technological innovation, geologists now have access to extensive 3D seismic surveys across sedimentary basins. Using these voluminous data sets to better understand subsurface complexity relies on developing seismic stratigraphic workflows that allow very high-resolution interpretation within a cost-effective timeframe. We have developed an innovative 3D seismic interpretation workflow that combines full-volume and semi-automated horizon tracking with high-resolution 3D seismic stratigraphic analysis. The workflow consists of converting data from seismic (two-way travelti

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Young, Anthony J., and Robert R. Coenraads. "A 3D seismic interpretation–Flounder Field, Gippsland Basin." Exploration Geophysics 18, no. 1-2 (1987): 235–38. http://dx.doi.org/10.1071/eg987235.

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Hossain, Muhammad Shahadat, Milovan Urosevic, and Anton Kepic. "Volumetric interpretation of 3D hard rock seismic data." ASEG Extended Abstracts 2013, no. 1 (2013): 1–3. http://dx.doi.org/10.1071/aseg2013ab088.

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12

Lecomte, Isabelle, Paul Lubrano Lavadera, Ingrid Anell, Simon J. Buckley, Daniel W. Schmid, and Michael Heeremans. "Ray-based seismic modeling of geologic models: Understanding and analyzing seismic images efficiently." Interpretation 3, no. 4 (2015): SAC71—SAC89. http://dx.doi.org/10.1190/int-2015-0061.1.

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Often, interpreters only have access to seismic sections and, at times, well data, when making an interpretation of structures and depositional features in the subsurface. The validity of the final interpretation is based on how well the seismic data are able to reproduce the actual geology, and seismic modeling can help constrain that. Ideally, modeling should create complete seismograms, which is often best achieved by finite-difference modeling with postprocessing to produce synthetic seismic sections for comparison purposes. Such extensive modeling is, however, not routinely affordable. A

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McClay, K. R., T. Dooley, P. Whitehouse, L. Fullarton, and S. Chantraprasert. "3D Analogue Models of Rift Systems: Templates for 3D Seismic Interpretation." Geological Society, London, Memoirs 29, no. 1 (2004): 101–15. http://dx.doi.org/10.1144/gsl.mem.2004.029.01.11.

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14

Wu, Xinming, and Dave Hale. "3D seismic image processing for unconformities." GEOPHYSICS 80, no. 2 (2015): IM35—IM44. http://dx.doi.org/10.1190/geo2014-0323.1.

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In seismic images, an unconformity can be first identified by reflector terminations (i.e., truncation, toplap, onlap, or downlap), and then it can be traced downdip to its corresponding correlative conformity, or updip to a parallel unconformity; for example, in topsets. Unconformity detection is a significant aspect of seismic stratigraphic interpretation, but most automatic methods work only in 2D and can only detect angular unconformities with reflector terminations. Moreover, unconformities pose challenges for automatic techniques used in seismic interpretation. First, it is difficult to

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15

White, D. J., and M. Malinowski. "Interpretation of 2D seismic profiles in complex geological terrains: Examples from the Flin Flon mining camp, Canada." GEOPHYSICS 77, no. 5 (2012): WC37—WC46. http://dx.doi.org/10.1190/geo2011-0478.1.

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A methodology was demonstrated for the 3D interpretation of networks of 2D seismic profiles in conjunction with other 3D geological constraints. The methodology employs 3D migration of 2D seismic data as a means of directly correlating reflections with out-of-plane geology, followed by ray-trace modeling of interpreted 3D geological surfaces. The proposed interpretation workflow was demonstrated with examples taken from 2D seismic profiles that were recently acquired for VMS ore exploration within the Flin Flon mining camp, Canada. In each example, the utility of the method was demonstrated an

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Ferreira, Rodrigo S., Emilio Vital Brazil, Reinaldo Silva, and Renato Cerqueira. "Seismic graph analysis to aid seismic interpretation." Interpretation 7, no. 3 (2019): SE81—SE92. http://dx.doi.org/10.1190/int-2018-0185.1.

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During the seismic interpretation process, geoscientists rely on their experience and visual analysis to assess the similarity between seismic sections. However, evaluating all of the seismic sections in a 3D survey can be a time-consuming task. When interpreters are working on a data set, a common procedure is to divide the cube in increasingly finer grids until they are satisfied with the result of the interpretation. We have developed a method based on graph theory and image texture in which we represent a seismic data set as a complete weighted undirected graph — which we call a seismic gr

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Hardage, Bob A. "Pitfall experiences when interpreting complex structure with low-quality seismic images." Interpretation 3, no. 1 (2015): SB29—SB37. http://dx.doi.org/10.1190/int-2014-0118.1.

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Structural interpretation of seismic data presents numerous opportunities for encountering interpretational pitfalls, particularly when a seismic image does not have an appropriate signal-to-noise ratio (S/N), or when a subsurface structure is unexpectedly complex. When both conditions exist — low S/N data and severe structural deformation — interpretation pitfalls are almost guaranteed. We analyzed an interpretation done 20 years ago that had to deal with poor seismic data quality and extreme distortion of strata. The lessons learned still apply today. Two things helped the interpretation tea

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Shafiq, Muhammad Amir, Zhen Wang, Ghassan AlRegib, Asjad Amin, and Mohamed Deriche. "A texture-based interpretation workflow with application to delineating salt domes." Interpretation 5, no. 3 (2017): SJ1—SJ19. http://dx.doi.org/10.1190/int-2016-0043.1.

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We propose a texture-based interpretation workflow and apply it to delineate salt domes in 3D migrated seismic volumes. First, we compute an attribute map using a novel seismic attribute, 3D gradient of textures (3D-GoT), which measures the dissimilarity between neighboring cubes around each voxel in a seismic volume across the time or depth, crossline, and inline directions. To evaluate the texture dissimilarity, we introduce five 3D perceptual and nonperceptual dissimilarity functions. Second, we apply a global threshold on the 3D-GoT volume to yield a binary volume and demonstrate its effec

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19

Li, Dong, Suping Peng, Yongxu Lu, Yinling Guo, and Xiaoqin Cui. "Seismic structure interpretation based on machine learning: A case study in coal mining." Interpretation 7, no. 3 (2019): SE69—SE79. http://dx.doi.org/10.1190/int-2018-0208.1.

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Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application of machine-learning algorithms to seismic interpretation can effectively solve these problems. We analyzed the theory and applicability of five machine-learning algorithms. Seismic forward modeling is a key connection between the model and seismic response, and it can obtain seismic data of known geologic structures. Based on the model

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Weindel, Richard L., Chris Carty, and Brenton Smith. "The Advantage of 3D visualization for 2d seismic interpretation." ASEG Extended Abstracts 2004, no. 1 (2004): 1–4. http://dx.doi.org/10.1071/aseg2004ab156.

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Dirstein, James K., and Gary N. Fallon. "Automated Interpretation of 3D seismic data using genetic algorithms." ASEG Extended Abstracts 2012, no. 1 (2012): 1. http://dx.doi.org/10.1071/aseg2012ab414.

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22

Boult, Pete, Brett Freeman, and Graham Yielding. "Structural Interpretation of seismic, geological realism and 3D thinking." ASEG Extended Abstracts 2016, no. 1 (2016): 1. http://dx.doi.org/10.1071/aseg2016ab2001.

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23

Adetokunbo, Peter, Abdullatif A. Al-Shuhail, and Saleh Al-Dossary. "3D seismic edge detection using magic squares and cubes." Interpretation 4, no. 3 (2016): T271—T280. http://dx.doi.org/10.1190/int-2015-0091.1.

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Edge detection is a category of geometric seismic attributes that has the capability to delineate vital information from seismic reflection data that can be used to aid qualitative and quantitative interpretation. We have evaluated a new method for geologic interpretation based on templates derived from magic squares and cubes. These are discrete differential operators that approximately calculate the spatial derivative of seismic amplitude through 2D and 3D convolution to locate edges and/or geologic features in seismic data. The new operator benefits from multidirectional scanning leading to

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24

Zhou, Binzhong, and Peter Hatherly. "Pushing coal seismic to its limits through computer aided interpretation and 3D seismics." Exploration Geophysics 31, no. 1-2 (2000): 343–46. http://dx.doi.org/10.1071/eg00343.

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25

Gao, Hang, Xinming Wu, and Guofeng Liu. "ChannelSeg3D: Channel simulation and deep learning for channel interpretation in 3D seismic images." GEOPHYSICS 86, no. 4 (2021): IM73—IM83. http://dx.doi.org/10.1190/geo2020-0572.1.

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Seismic channel interpretation involves detecting channel structures, which often appear as meandering shapes in 3D seismic images. Many conventional methods are proposed for delineating channel structures using different seismic attributes. However, these methods are often sensitive to seismic discontinuities (e.g., noise and faults) that are not related to channels. We have adopted a convolutional neural network (CNN) method to improve automatic channel interpretation. The key problem in applying the CNN method into channel interpretation is the absence of labeled field seismic images for tr

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26

Hale, Dave. "Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images." GEOPHYSICS 78, no. 2 (2013): O33—O43. http://dx.doi.org/10.1190/geo2012-0331.1.

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Fault interpretation enhances our understanding of complex geologic structures and stratigraphy apparent in 3D seismic images. Common steps in this interpretation include image processing to highlight faults, the construction of fault surfaces, and estimation of fault throws. Although all three of these steps have been automated to some extent by others, fault interpretation today typically requires significant manual effort, suggesting that further improvements in automatic methods are feasible and worthwhile. I first used an efficient algorithm to compute images of fault likelihoods, strikes

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27

Fehmers, Gijs C., and Christian F. W. Höcker. "Fast structural interpretation with structure‐oriented filtering." GEOPHYSICS 68, no. 4 (2003): 1286–93. http://dx.doi.org/10.1190/1.1598121.

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We present a new approach to structural interpretation of 3D seismic data with the objectives of simplifying the task and reducing the interpretation time. The essential element is the stepwise removal of noise, and eventually of small‐scale stratigraphic and structural features, to derive more and more simple representations of structural shape. Without noise and small‐scale structure, both man and machine (autotrackers) can arrive at a structural interpretation faster. If the interpreters so wish, they can refine such an initial crude structural interpretation in selected target areas. We di

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Gao, Dengliang. "3D seismic volume visualization and interpretation: An integrated workflow with case studies." GEOPHYSICS 74, no. 1 (2009): W1—W12. http://dx.doi.org/10.1190/1.3002915.

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One of the major problems in subsurface seismic exploration is the uncertainty (nonuniqueness) in geologic interpretation because of the complexity of subsurface geology and the limited dimension of the data available. Case studies from worldwide exploration projects indicate that an integrated, three-dimensional (3D) seismic volume visualization and interpretation workflow contributes to resolving the problem by mining and exposing critical geologic information from within seismic data volumes. Following 3D seismic data acquisition and processing, the interpretation workflow consists of four

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Ajjabou, Leïla, Maëlle Bourdais, Natalia Gritsajuk, Sébastien Lacaze, and Jean-Philippe Adam. "Interpretative filtering." APPEA Journal 57, no. 2 (2017): 687. http://dx.doi.org/10.1071/aj16124.

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Industry already acknowledges the power of dip-steered median filters to clean-up seismic data volume in which coherent events are enhanced and random noise is reduced. By combining two powerful technologies, this paper presents a dip-steered geostatistical filtering solution, called interpretative filtering (IF), which gives remarkable results for removing random and organised noise from seismic volumes. It defines a new generation of spatial filters useful for processing and interpretation. The IF solution is based on a 3D non-stationary factorial kriging technique (M-GS technology) driven b

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Ha, Thang N., Kurt J. Marfurt, Bradley C. Wallet, and Bryce Hutchinson. "Pitfalls and implementation of data conditioning, attribute analysis, and self-organizing maps to 2D data: Application to the Exmouth Plateau, North Carnarvon Basin, Australia." Interpretation 7, no. 3 (2019): SG23—SG42. http://dx.doi.org/10.1190/int-2018-0248.1.

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Recent developments in attribute analysis and machine learning have significantly enhanced interpretation workflows of 3D seismic surveys. Nevertheless, even in 2018, many sedimentary basins are only covered by grids of 2D seismic lines. These 2D surveys are suitable for regional feature mapping and often identify targets in areas not covered by 3D surveys. With continuing pressure to cut costs in the hydrocarbon industry, it is crucial to extract as much information as possible from these 2D surveys. Unfortunately, much if not most modern interpretation software packages are designed to work

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Di, Haibin, and Dengliang Gao. "Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation." Interpretation 5, no. 3 (2017): SJ31—SJ40. http://dx.doi.org/10.1190/int-2016-0214.1.

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Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the o

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Luo, Simon, and Dave Hale. "Unfaulting and unfolding 3D seismic images." GEOPHYSICS 78, no. 4 (2013): O45—O56. http://dx.doi.org/10.1190/geo2012-0350.1.

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Identifying and extracting geologic horizons is useful for interpretation of stratigraphic features as well as analysis of structural deformation. To extract horizons from a seismic image, we developed methods for automatically unfaulting and unfolding an image to restore all horizons to an undeformed, horizontal state. First, using fault surfaces and dip-separation vectors estimated from an image, we interpolated dip-separation vectors at locations between fault surfaces, and then we used the interpolated dip-separation vectors to unfault an image. Then, using a method for automatic seismic i

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33

Maerten, Frantz, and Laurent Maerten. "On a method for reducing interpretation uncertainty of poorly imaged seismic horizons and faults using geomechanically based restoration technique." Interpretation 3, no. 4 (2015): SAA105—SAA116. http://dx.doi.org/10.1190/int-2015-0009.1.

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To reduce exploration risk and optimize production in structurally complex areas, the geologic interpretation must be based on sound geomechanical principles. Despite advances in 3D seismic acquisition and processing techniques as well as in the availability of computationally robust interpretation software, the challenge associated with interpreting complex structures from seismic reflection data is that highly deformed areas surrounding faults, folds, and salt surfaces are often poorly imaged and therefore their interpretation is highly uncertain. We have developed a methodology that should

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Marfurt, Kurt J., and Tiago M. Alves. "Pitfalls and limitations in seismic attribute interpretation of tectonic features." Interpretation 3, no. 1 (2015): SB5—SB15. http://dx.doi.org/10.1190/int-2014-0122.1.

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Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small

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35

ASAKURA, NATSUO. "3D seismic interpretation integrated by VSP and well log data." Journal of the Japanese Association for Petroleum Technology 51, no. 1 (1986): 2–15. http://dx.doi.org/10.3720/japt.51.2.

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36

Chopra, Satinder, and Kurt J. Marfurt. "Volumetric curvature attributes add value to 3D seismic data interpretation." Leading Edge 26, no. 7 (2007): 856–67. http://dx.doi.org/10.1190/1.2756864.

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Li, Pan, Zhao Jun Zhou, and Meng Huang. "Study on 3D Seismic Data Field Hybrid Rendering Technique of Natural Gas Hydrate." Applied Mechanics and Materials 539 (July 2014): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amm.539.161.

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3D visualization technology is a tool used for displaying, describing, and understanding the characteristics of geologic bodies, and features high efficiency, objective accuracy, visual expression, etc. In this paper, the man-machine interactive interpretation and 3D visualization technology rapidly displaying and analyzing the 3D seismic data of hydrate ore volume is researched and developed using the hybrid rendering technique. Through the integrated interpretation on the 3D space structure, stratum, and seismic attributes, the visualized multi-attribute superimposition analysis is implement

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38

Bennett, Alexandra. "Exploring for stratigraphic traps in the Patchawarra Formation, Cooper Basin: an integrated seismic methodology." APPEA Journal 58, no. 2 (2018): 779. http://dx.doi.org/10.1071/aj17089.

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The Patchawarra Formation is characterised by Permian aged fluvial sediments. The conventional hydrocarbon play lies within fluvial sandstones, attributed to point bar deposits and splays, that are typically overlain by floodbank deposits of shales, mudstones and coals. The nature of the deposition of these sands has resulted in the discovery of stratigraphic traps across the Western Flank of the Cooper Basin, South Australia. Various seismic techniques are being used to search for and identify these traps. High seismic reflectivity of the coals with the low reflectivity of the relatively thin

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Guo, Yinling, Suping Peng, Wenfeng Du, and Dong Li. "Fault and horizon automatic interpretation by CNN: a case study of coalfield." Journal of Geophysics and Engineering 17, no. 6 (2020): 1016–25. http://dx.doi.org/10.1093/jge/gxaa060.

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Abstract A convolutional neural network (CNN) is a powerful tool used for seismic interpretation. It does not require manual intervention and can automatically detect geological structures using the pattern features of the original seismic data. In this study, we presented the development history of seismic interpretation and the application of CNN in seismic exploration. We proposed a set of CNN prediction methods and processes for coalfield seismic interpretation and realised automatic interpretation of faults and horizons based on the relationship between faults and horizons. We defined a C

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Acuña-Uribe, Mateo, María Camila Pico-Forero, Paul Goyes-Peñafiel, and Darwin Mateus. "Enhanced ant tracking: Using a multispectral seismic attribute workflow to improve 3D fault detection." Leading Edge 40, no. 7 (2021): 502–12. http://dx.doi.org/10.1190/tle40070502.1.

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Fault interpretation is a complex task that requires time and effort on behalf of the interpreter. Moreover, it plays a key role during subsurface structural characterization either for hydrocarbon exploration and development or well planning and placement. Seismic attributes are tools that help interpreters identify subsurface characteristics that cannot be observed clearly. Unfortunately, indiscriminate and random seismic attribute use affects the fault interpretation process. We have developed a multispectral seismic attribute workflow composed of dip-azimuth extraction, structural filterin

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Hart, Bruce S. "Whither seismic stratigraphy?" Interpretation 1, no. 1 (2013): SA3—SA20. http://dx.doi.org/10.1190/int-2013-0049.1.

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Here, I provide an historical summary of seismic stratigraphy and suggest some potential avenues for future collaborative work between sedimentary geologists and geophysicists. Stratigraphic interpretations based on reflection geometry- or shape-based approaches have been used to reconstruct depositional histories and to make qualitative and (sometimes) quantitative predictions of rock physical properties since at least the mid-1970s. This is the seismic stratigraphy that is usually practiced by geology-focused interpreters. First applied to 2D seismic data, interest in seismic stratigraphy wa

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Guan, Xiaowei, Qian Meng, Chuanjin Jiang, Xinyu Liu, and Menglu Han. "Research and Application of Globally Optimized Sequence Stratigraphic Seismic Interpretation Technology: Taking the Lower Cretaceous Shahezi Formation of Xujiaweizi Fault Depression as an Example." Geofluids 2021 (September 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/7564374.

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In the study of sequence stratigraphy in continental rift basins, the use of seismic data to track different levels of sequence stratigraphic boundaries laterally is the key to the division of sequence stratigraphic units at all levels and the establishment of an isochronous sequence stratigraphic framework. Traditional seismic interpretation and the establishment of a 3D sequence stratigraphic structure model are a difficult research work. This paper introduces the concept of cost function minimization and performs global stratigraphic scanning on 3D seismic data to interpret horizons and fau

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Alves, Tiago M., Kamal’deen Omosanya, and Phalene Gowling. "Volume rendering of enigmatic high-amplitude anomalies in southeast Brazil: A workflow to distinguish lithologic features from fluid accumulations." Interpretation 3, no. 2 (2015): A1—A14. http://dx.doi.org/10.1190/int-2014-0106.1.

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High-quality 3D seismic data are used to extract and isolate high-amplitude anomalies so that fluid-related features, magmatic intrusions, and mass-transport deposits can be interpreted. The use of advanced seismic interpretation tools such as volume rendering and attribute extraction replaces the “traditional” horizon mapping of high-amplitude anomalies. In this work we show that the geometry of anomalies is better constrained when seismic attributes can be imaged and interpreted in three dimensions. Volume-rendering techniques are less laborious, reduce interpretation time, and to a large ex

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Paton, Gaynor S., and Jonathan Henderson. "Visualization, interpretation, and cognitive cybernetics." Interpretation 3, no. 3 (2015): SX41—SX48. http://dx.doi.org/10.1190/int-2014-0283.1.

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Interpretation of 3D seismic data involves the analysis and integration of many forms and derivatives of the original reflectivity data. This can lead to the generation of an overwhelming amount of data that can be difficult to use effectively when relying on conventional interpretation techniques. Our natural cognitive processes have evolved so that we can absorb and understand large amounts of complex data extremely quickly and effectively. However, these cognitive processes are heavily influenced by context and color perception. Seismic interpretation can benefit greatly through better expl

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Fang, Yong, Wenshan Luo, Xiaoxia Luo, Xukui Feng, Bo Zhao, and Wenrui Jia. "Applying integrated seismic technology to complex foothill areas of foreland basins in China." Leading Edge 38, no. 8 (2019): 597–603. http://dx.doi.org/10.1190/tle38080597.1.

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Due to complicated near-surface conditions, including large elevation changes and complex geologic structures, accurate imaging of subsurface structures for hydrocarbon exploration in the foreland basins of western China has been challenging for many years. After decades of research and fieldwork, we developed an effective seismic exploration workflow that uses the latest technologies from acquisition to imaging. They include 3D high-density and wide-azimuth (WAZ) acquisition, 3D true-surface tilted transverse isotropy (TTI) anisotropic prestack depth migration, and dual-detachment structural

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Gol, E. М., and N. S. Avdeev. "Wave Velocity Ratio Analysis in the 3D/3C Multicomponent Seismic Interpretation." Oil and Gas technologies 120, no. 1 (2019): 32–37. http://dx.doi.org/10.32935/1815-2600-2019-120-1-32-37.

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Yuan, Shukun, Michael V. DeAngelo, and Bob A. Hardage. "Interpretation of fractures and joint inversion using multicomponent seismic data — Marcellus Shale example." Interpretation 2, no. 2 (2014): SE55—SE62. http://dx.doi.org/10.1190/int-2013-0146.1.

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Evaluating and exploiting unconventional complex oil and gas reservoirs such as the Marcellus Shale gas reservoirs within the Appalachian Basin in Pennsylvania, USA, have gained considerable interest in recent years. Technologies such as conventional 3D seismic, horizontal drilling, and hydraulic fracturing have been at the forefront of the effort to exploit these resources. Recently, multicomponent seismic technologies have been integrated into some resource evaluation and reservoir characterization activities of low-permeability rock systems. We evaluated how multicomponent seismic technolog

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Wu, Xinming, Simon Luo, and Dave Hale. "Moving faults while unfaulting 3D seismic images." GEOPHYSICS 81, no. 2 (2016): IM25—IM33. http://dx.doi.org/10.1190/geo2015-0381.1.

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Unfaulting seismic images to correlate seismic reflectors across faults is helpful in seismic interpretation and is useful for seismic horizon extraction. Methods for unfaulting typically assume that fault geometries need not change during unfaulting. However, for seismic images containing multiple faults and, especially, intersecting faults, this assumption often results in unnecessary distortions in unfaulted images. We have developed two methods to compute vector shifts that simultaneously move fault blocks and the faults themselves to obtain an unfaulted image with minimal distortions. For

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Burnett, William A., Alexander Klokov, Sergey Fomel, Rishidev Bansal, Enru Liu, and Tim Jenkinson. "Seismic diffraction interpretation at Piceance Creek." Interpretation 3, no. 1 (2015): SF1—SF14. http://dx.doi.org/10.1190/int-2014-0091.1.

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We applied time-domain seismic diffraction imaging to a 3D data set from the Piceance Creek Field, Piceance Basin, northwest Colorado. The work was motivated by the need for insight into natural fracture distribution, thought to influence production. We used a novel chain of two previously developed processing steps to separate diffractions from the recorded wavefield — One step is applied to the conventional stack volume, and the other was applied to migrated dip-angle gathers. The diffractions were then imaged independently for interpretation. Comparison of seismic attributes, commonly used

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Ebuna, Daniel R., Jared W. Kluesner, Kevin J. Cunningham, and Joel H. Edwards. "Statistical approach to neural network imaging of karst systems in 3D seismic reflection data." Interpretation 6, no. 3 (2018): B15—B35. http://dx.doi.org/10.1190/int-2017-0197.1.

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The current lack of a robust standardized technique for geophysical mapping of karst systems can be attributed to the complexity of the environment and prior technological limitations. Abrupt lateral variations in physical properties that are inherent to karst systems generate significant geophysical noise, challenging conventional seismic signal processing and interpretation. The application of neural networks (NNs) to multiattribute seismic interpretation can provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambi

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