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1
Roberts, D. G. "3D Seismic Interpretation." Marine and Petroleum Geology 21, no. 3 (March 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 (July 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 (April 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 (May 1, 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 automatic segmentation, allowing for interpreter insight to be combined with modern computational capabilities. A successful 3D field data example demonstrates that this method could become an important tool for interactive interpretation tasks.
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Gao, Dengliang. "Volume texture extraction for 3D seismic visualization and interpretation." GEOPHYSICS 68, no. 4 (July 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 the characterization and identification of certain geological features. Here, I present a volume texture extraction method to overcome these limitations. In a two‐dimensional (2D) image domain where data samples are visualized by pixels (picture elements), a texture has been typically characterized based on a planar texel (textural element) using a gray level co‐occurrence matrix. I extend the concepts to a 3D seismic domain, where reflection amplitudes are visualized by voxels (volume picture elements). By evaluating a voxel co‐occurrence matrix (VCM) based on a cubic texel at each of the voxel locations, the algorithm extracts a plurality of volume textural attributes that are difficult to obtain using conventional seismic attribute extraction algorithms. Case studies indicate that the VCM texture extraction method helps visualize and detect major structural and stratigraphic features that are fundamental to robust seismic interpretation and successful hydrocarbon exploration.
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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 (April 21, 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 relatively simple in their CNN architectures, which primary rely on the seismic amplitude but undesirably fail to fully use the preknown geologic knowledge and/or solid interpretational rules of an experienced interpreter who works on the same task. We have developed a generally applicable framework for integrating a seismic interpretation CNN with such commonly used knowledge and rules as constraints. Three example use cases, including relative geologic time-guided facies analysis, layer-customized fault detection, and fault-oriented stratigraphy mapping, are provided for illustrating how one or more constraints can be technically imposed and demonstrating what added values such a constrained CNN can bring. It is concluded that the imposition of interpretational constraints is capable of improving CNN-assisted seismic interpretation and better assisting the tasks of subsurface mapping and modeling.
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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 (May 1, 2020): 17–25. http://dx.doi.org/10.46717/igj.53.1d.2rw-2020-05-01.
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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 (July 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 these increasingly large seismic data sets with machine learning. We are able to perform typical seismic interpretation tasks such as mapping tectonic faults, salt bodies, and sedimentary horizons at high accuracy using deep convolutional neural networks. We share our workflows and scripts, encouraging users to apply our methods to similar problems. Our methodology is generic and flexible, allowing an easy adaptation without major changes. Once trained, these models can analyze large volumes of data within seconds, opening a new pathway to study the processes shaping the internal structure of our planet.
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Paumard, Victorien, Julien Bourget, Benjamin Durot, Sébastien Lacaze, Tobi Payenberg, Annette D. George, and Simon Lang. "Full-volume 3D seismic interpretation methods: A new step towards high-resolution seismic stratigraphy." Interpretation 7, no. 3 (August 1, 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 traveltime) to a relative geological time (RGT) volume, in which a relative geological age is assigned to each point of the volume. The generation of a horizon stack is used to extract an unlimited number of chronostratigraphic surfaces (i.e., seismic horizons). Integrated stratigraphic tools may be used to navigate throughout the 3D seismic data to pick seismic unconformities using standard seismic stratigraphic principles in combination with geometric attributes. Here, we applied this workflow to a high-quality 3D seismic data set located in the Northern Carnarvon Basin (North West Shelf, Australia) and provided an example of high-resolution seismic stratigraphic interpretation from an Early Cretaceous shelf-margin system (Lower Barrow Group). This approach is used to identify 73 seismic sequences (i.e., clinothems) bounded by 74 seismic unconformities. Each clinothem presents an average duration of approximately 63,000 years (fifth stratigraphic order), which represents an unprecedented scale of observation for a Cretaceous depositional system on seismic data. This level of interpretation has a variety of applications, including high-resolution paleogeographical reconstructions and quantitative analysis of subsurface data. This innovative workflow constitutes a new step in seismic stratigraphy because it enables interpreters to map seismic sequences in a true 3D environment by taking into account the full variability of depositional systems at high frequency through time and space.
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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 (March 1, 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 (December 2013): 1–3. http://dx.doi.org/10.1071/aseg2013ab088.
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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 (November 1, 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 far more efficient option, using the simpler 1D convolution model with reflectivity logs extracted along verticals in velocity models, generates poor modeling results when lateral velocity variations are expected. A third and intermediate option is to use the various ray-based approaches available, which are efficient and flexible. However, standard ray methods, such as the normal-incidence point for unmigrated poststack sections or image rays for simulating time-migrated poststack results, cannot deal with complex and detailed targets, and will not reproduce the realistic (3D) resolution effects of seismic imaging. Nevertheless, ray methods can also be used to estimate 3D spatial prestack convolution operators, so-called point-spread functions. These are functions of the survey, velocity model, and wavelet, among others, and therefore they include 3D angle-dependent illumination and resolution effects. Prestack depth migration images are thus rapidly simulated by spatial convolution with detailed 3D reflectivity models, which goes far beyond the limits of 1D convolution modeling. This 3D convolution modeling should allow geologists to better assess their interpretations and draw more definitive conclusions.
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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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Wu, Xinming, and Dave Hale. "3D seismic image processing for unconformities." GEOPHYSICS 80, no. 2 (March 1, 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 accurately estimate normal vectors or slopes of seismic reflectors at an unconformity with multioriented structures due to reflector terminations. Second, seismic flattening methods cannot correctly flatten reflectors at unconformities that represent hiatuses or geologic age gaps. We have developed a 3D unconformity attribute computed from a seismic amplitude image to detect unconformities by highlighting the angular unconformities and corresponding parallel unconformities or correlative conformities. These detected unconformity surfaces were further used as constraints for a structure-tensor method to more accurately estimate seismic normal vectors at unconformities. Finally, using detected unconformities as constraints and more accurate normal vectors, we could better flatten seismic images with unconformities.
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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 (September 1, 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 and the resulting inferences were validated by comparison with a true 3D seismic survey acquired over a subset of the same area.
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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 (August 1, 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 graph. The vertices of this graph represent the seismic sections, and the weight of the edges represents the distance between the texture feature vectors of the vertices they connect, allowing for a powerful yet concise representation of potentially large data sets. We have investigated the potential of graph analysis to build an adaptive grid that is more likely to capture the underlying structures present in a survey, providing a tool for a faster and more precise interpretation. The main idea is that such a grid would be finer in regions with more geologic variations and coarser otherwise. To demonstrate the capabilities of our technique, we apply it on a public data set called Netherlands F3. Using our method, we suggest which seismic sections — key sections — should be considered in the interpretation process. The results of our experiments indicate that our methodology has great potential to aid the seismic interpretation process.
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Hardage, Bob A. "Pitfall experiences when interpreting complex structure with low-quality seismic images." Interpretation 3, no. 1 (February 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 team develop a viable structural model of the prospect. First, existing industry-accepted formation tops assigned to regional wells were rejected and new log interpretations were done to detect evidence of repeated sections and overturned strata. Second, the frequency content of the 3D seismic data volume was restricted to only the first octave of its seismic spectrum to create better evidence of fault geometries. A logical and workable structural interpretation resulted when these two action steps were taken. To the knowledge of our interpretation team, neither of these approaches had been attempted in the area at the time of this work (early 1990s). We found two pitfalls that may be encountered by other interpreters. The first pitfall was the hazard of accepting long-standing, industry-accepted definitions of the positions of formation tops on well logs. This nonquestioning acceptance of certain log signatures as indications of targeted formation tops led to a serious misinterpretation in our study. The second pitfall was the prevailing passion by geophysicists to create seismic data volumes that have the widest possible frequency spectrum. This interpretation effort showed that the opposite strategy was better at this site and for our data conditions; i.e., it was better to filter seismic images so that they contained only the lowest octave of frequencies in the seismic spectrum.
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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 (August 31, 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 effects on salt-dome delineation using objective evaluation measures such as receiver operating characteristic curves and the areas under the curves. Third, with an initial seed point selected inside the binary volume, we use a 3D region growing method to capture a salt body. For an automated 3D region growing, we adopt a tensor-based automatic seed point selection method. Finally, we apply morphological postprocessing to delineate the salt dome within the seismic volume. Furthermore, we also develop an objective evaluation measure based on the curvature and shape to compute the similarity between detected salt-dome boundaries and the reference interpreted by the geophysicist. Experimental results on a real data set from the North Sea show that the proposed method outperforms the state-of-the-art methods for salt-dome delineation.
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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 (August 1, 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 modeling data, we first optimized the seismic attributes sensitive to the target geologic structure and then we verified the accuracy of the five machine-learning algorithms by the cross-checking method. In this case, the random forest algorithm had the highest accuracy. So we examined the structural interpretation method based on a random forest using the 3D seismic reflection data from coalfield exploration. The prediction effect of this interpretation workflow is verified by comparison with known geologic structures on the plane and profile. The results suggest that the random forest algorithm is feasible to indicate geologic structure interpretations in the case of collapsed column and fault structures and it can effectively improve the efficiency of seismic interpretation and its accuracy. The machine-learning-based workflow provides a new technique for seismic structure interpretation in coal mining.
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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 (December 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 (December 2012): 1. http://dx.doi.org/10.1071/aseg2012ab414.
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Boult, Pete, Brett Freeman, and Graham Yielding. "Structural Interpretation of seismic, geological realism and 3D thinking." ASEG Extended Abstracts 2016, no. 1 (December 2016): 1. http://dx.doi.org/10.1071/aseg2016ab2001.
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Adetokunbo, Peter, Abdullatif A. Al-Shuhail, and Saleh Al-Dossary. "3D seismic edge detection using magic squares and cubes." Interpretation 4, no. 3 (August 1, 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 efficient detection of different edge locations and their respective orientations. We have tested the new operators against the commonly used Sobel filter using two 3D seismic data volumes. Results of the [Formula: see text] magic cube operators provided better definition of seismic features than the [Formula: see text] magic cube operators. The overall results compared favorably with the Sobel operator, which suggests that the method can serve as a complementary tool to other existing seismic attributes.
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Zhou, Binzhong, and Peter Hatherly. "Pushing coal seismic to its limits through computer aided interpretation and 3D seismics." Exploration Geophysics 31, no. 1-2 (March 2000): 343–46. http://dx.doi.org/10.1071/eg00343.
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Gao, Hang, Xinming Wu, and Guofeng Liu. "ChannelSeg3D: Channel simulation and deep learning for channel interpretation in 3D seismic images." GEOPHYSICS 86, no. 4 (June 10, 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 training the CNNs. To solve this problem, we adopt a workflow to automatically generate numerous synthetic training data sets with realistic channel structures. In this workflow, we first randomly simulate various meandering channel models based on geologic numerical simulation. We further simulate structural deformation in the form of stratigraphic folding referred to as “folding structures” and combine them with the previously generated channel models to create reflectivity models and the corresponding channel labels. Convolved with a wavelet, the reflectivity models can be transformed into learnable synthetic seismic volumes. By training the designed CNN with synthetic seismic data, we obtain a CNN that learns the characterization of channel structures. Although trained on only synthetic seismic volumes, this CNN shows outstanding performance on field seismic volumes. This indicates that the synthetic seismic images created in this workflow are realistic enough to train the CNN for channel interpretation in field seismic images.
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Hale, Dave. "Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images." GEOPHYSICS 78, no. 2 (March 1, 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, and dips from a 3D seismic image. From these three fault images, I then automatically extracted fault surfaces as meshes of quadrilaterals that coincide with ridges of fault likelihood. A quadrilateral mesh is a simple data structure alongside which one can easily gather samples of the 3D seismic image. I automatically estimated fault throws by minimizing differences in values of samples gathered from opposite sides of a fault, while constraining the variation of throw within a fault surface. I tested the fidelity of estimated fault throws by using them to undo faulting. After unfaulting, reflectors in 3D seismic images were more continuous than those in the original 3D seismic image. In one example, this unfaulting test supported the observation that some extracted fault surfaces have unusual conical shapes.
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Fehmers, Gijs C., and Christian F. W. Höcker. "Fast structural interpretation with structure‐oriented filtering." GEOPHYSICS 68, no. 4 (July 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 discuss a class of filters that removes noise and, if desired, simplifies structural information in 3D seismic data. The gist of these filters is a smoothing operation parallel to the seismic reflections that does not operate beyond reflection terminations (faults). These filters therefore have three ingredients: (1) orientation analysis, (2) edge detection, and (3) edge‐preserving oriented smoothing. We discuss one particular implementation of this principle in some detail: a simulated anisotropic diffusion process (low‐pass filter) that diffuses the seismic amplitude while the diffusion tensor is computed from the local image structure (so that the diffusion is parallel to the reflections). Examples show the remarkable effects of this operation.
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Gao, Dengliang. "3D seismic volume visualization and interpretation: An integrated workflow with case studies." GEOPHYSICS 74, no. 1 (January 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 integrated phases from data selection and conditioning, to structure and facies characterization, to prospect evaluation and generation, to well-bore planning. In the data selection and conditioning phase, the most favored and frequently used data are the full-angle, limited-angle, and limited-azimuth stack amplitude with significant structure and facies enhancements. Signal-to-noise ratio, color scheme, dynamic range, bit resolution, and visual contrast all affect thevisibility of features of interest. In the structure and facies characterization phase, vertical slicing along arbitrary traverses demonstrates structure styles, stratigraphic architecture, and reservoir geometry in the cross-sectional view. Time/depth slicing defines lateral and vertical variability in the structural trend and areal extent in the map view. Stratal slicing and fault slicing map chronostratigraphic seismic facies and cross-stratal, along-fault seismic signature. Volume flattening and structure restoration aid in unraveling paleostructural framework and stratigraphic architecture and their growth histories. In the prospect evaluation and generation phase, a combination of volume trimming, co-rendering, transparency, attribute analysis, and attribute-body detection is instrumental in delineating volumetric extent and evaluating spatial connectivity of critical seismic features. Finally, in the well-bore planning phase, informed decision-making relies on the integration of all the information and knowledge interrogated from 3D seismic data. Most importantly, interpreters’ geologic insight and play concept are crucial to optimal well-bore planning with high geologic potential and low economic risk.
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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 by a high-resolution interpretation of the seismic volume coming from a Relative Geological Time model. This new technique enables some non-stationary noise to be dealt with. For example acquisition footprints are well known to be non-stationary noises, with varying orientation, width and intensity as a function of the position in the volume. IF opens the way to refine filtering operations of seismic volumes even in complex structural environments. For this paper, the solution is applied to an open-file 3D marine seismic dataset (HCA2000A 3D) covering the Exmouth sub-basin, North West Shelf, Australia. This survey presents a dense fault system, mostly intersecting the Jurassic and the Cretaceous. Results show a great improvement on reducing the acquisition footprint and random noise without compromising the definition of the subtle geological features.
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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 (August 1, 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 exclusively with 3D data. To determine if we can apply 3D volumetric interpretation workflows to grids of 2D seismic lines, we have applied data conditioning, attribute analysis, and a machine-learning technique called self-organizing maps to the 2D data acquired over the Exmouth Plateau, North Carnarvon Basin, Australia. We find that these workflows allow us to significantly improve image quality, interpret regional geologic features, identify local anomalies, and perform seismic facies analysis. However, these workflows are not without pitfalls. We need to be careful in choosing the order of filters in the data conditioning workflow and be aware of reflector misties at line intersections. Vector data, such as reflector convergence, need to be extracted and then mapped component-by-component before combining the results. We are also unable to perform attribute extraction along a surface or geobody extraction for 2D data in our commercial interpretation software package. To address this issue, we devise a point-by-point attribute extraction workaround to overcome the incompatibility between 3D interpretation workflow and 2D data.
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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 (August 31, 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 original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.
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Luo, Simon, and Dave Hale. "Unfaulting and unfolding 3D seismic images." GEOPHYSICS 78, no. 4 (July 1, 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 image flattening, we unfolded the unfaulted image to obtain a new image in which sedimentary layering is horizontal and also aligned across faults. From this unfaulted and unfolded image, we automatically extracted geologic horizons.
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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 (November 1, 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 help geophysicists quickly check the strengths and weaknesses of their interpretation and to automatically reduce the uncertainty in a faulted horizon geometry. Our workflow consisted of restoring interpreted seismic horizons and relating the concentrations of computed deformation attributes to areas of interpretation uncertainty. We used the technique based on an iterative finite-element formulation that allowed unfolding and unfaulting of 3D horizons using physical elastic behavior. A fast algorithm has been developed to automatically correct the interpreted structures in zones that exhibited anomalous deformation concentrations after restoration. This approach is able to mechanically check and reduce uncertainty in a faulted seismic horizon interpretation. Its application to synthetic and reservoir data has a high degree of reliability in the characterization of structurally complex reservoirs. This technique is also applicable to 2D models (geologic cross sections) and 3D models (volume).
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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 (February 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 processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.
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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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Chopra, Satinder, and Kurt J. Marfurt. "Volumetric curvature attributes add value to 3D seismic data interpretation." Leading Edge 26, no. 7 (July 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 implemented for describing the spatial distribution characteristics of hydrate ore volume and exquisitely describing the subtle geological characteristics of hydrate ore volume. By the hybrid rendering technique, authentication and interpretation are provided for the geological exploration work, so as to greatly enhance the visualization and accuracy of the geological analysis, and also provide a good decision-making foundation for the subsequent development of resources.
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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 sands, often below seismic resolution, masks a reservoir response. These factors, combined with complex geometry of these reservoirs, prove a difficult play to image and interpret. Standard seismic interpretation has proven challenging when attempting to map fluvial sands. Active project examples within a 196 km2 3D seismic survey detail an evolving seismic interpretation methodology, which is being used to improve the delineation of potential stratigraphic traps. This involves an integration of seismic processing, package mapping, seismic attributes and imaging techniques. The integrated seismic interpretation methodology has proven to be a successful approach in the discovery of stratigraphic and structural-stratigraphic combination traps in parts of the Cooper Basin and is being used to extend the play northwards into the 3D seismic area discussed.
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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 (December 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 CNN model training method based on structural geological modelling, which allowed rapid and accurate establishment of fault and horizon labels by using structural modelling. We used two examples to verify the accuracy of the algorithm, one to test for synthetic 3D seismic data and one to test for real coalfield seismic data. The results showed that CNNs can effectively predict both faults and horizons at the same time and has high accuracy. Thus, CNNs are potentially novel interpretation tools for coalfield seismic interpretation.
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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 (July 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 filtering, frequency filtering, detection of amplitude discontinuities, enhancement of amplitude discontinuities, and automatic fault extraction. The result is an enhanced ant-tracking volume in which faults are improved compared to common fault-enhanced workflows that incorporate the ant-tracking algorithm. To prove the effectiveness of the enhanced ant-tracking volume, we have applied this methodology in three seismic volumes with different random noise content and seismic characteristics. The detected and extracted faults are continuous, clean, and accurate. The proposed fault identification workflow reduces the effort and time spent in fault interpretation as a result of the integration and appropriate use of various types of seismic attributes, spectral decomposition, and swarm intelligence.
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Hart, Bruce S. "Whither seismic stratigraphy?" Interpretation 1, no. 1 (August 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 was reinvigorated by the development of seismic geomorphology on 3D volumes. This type of reflection geometry/shape-based interpretation strategy is a fairly mature science that includes seismic sequence analysis, seismic facies analysis, reflection character analysis, and seismic geomorphology. Rock property predictions based on seismic stratigraphic interpretations usually are qualitative, and reflection geometries commonly may permit more than one interpretation. Two geophysics-based approaches, practiced for nearly the same length of time as seismic stratigraphy, have yet to gain widespread adoption by geologic interpreters even though they have much potential application. The first is the use of seismic attributes for “feature detection,” i.e., helping interpreters to identify stratigraphic bodies that are not readily detected in conventional amplitude displays. The second involves rock property (lithology, porosity, etc.) predictions from various inversion methods or seismic attribute analyses. Stratigraphers can help quality check the results and learn about relationships between depositional features and lithologic properties of interest. Stratigraphers also can contribute to a better seismic analysis by helping to define the effects of “stratigraphy” (e.g., laminations, porosity, bedding) on rock properties and seismic responses. These and other seismic-related pursuits would benefit from enhanced collaboration between sedimentary geologists and geophysicists.
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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 faults in a large grid. Constrained by the results, human-computer interactive intelligent interpretation, by adding iterative interpretation of geological knowledge, established a global stratigraphic model with a relative geological age. The application in the Lower Cretaceous Shahezi Formation of Xujiaweizi fault depression shows that this technology has improved the accuracy and efficiency of sequence stratigraphic interpretation, and the application of this technology has achieved the interpretation of each event horizon under the current seismic data resolution conditions. In this way, a continuous sequence stratigraphic model is established. From this stratigraphic model, any high-frequency sequence-interpreted seismic horizon can be extracted, which provides a basis for the combination of lateral resolution and longitudinal resolution of subsequent reservoir prediction.
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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 (May 1, 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 extent remove interpretation biases. To demonstrate the advantages of our approach, we analyze three types of anomalies in southeast Brazil. In the study area, unconformable “soft-on-hard” anomalies are related to fluid accumulations, whereas igneous sills show signature tabular and concave geometries. We also question the existence of sill-to-sill junctions in the study area, otherwise interpreted by conventional interpretation methods, based on the 3D rendering techniques described. Hence, we theorize that the appearance of the junctions on seismic data from other basins can be a consequence of overlapping sill tips, resulting in the constructive interference of their seismic signals.
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Paton, Gaynor S., and Jonathan Henderson. "Visualization, interpretation, and cognitive cybernetics." Interpretation 3, no. 3 (August 1, 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 exploiting the positive aspects of visual cognition and through techniques designed to minimize the pitfalls inherent in the cognitive process. The interpretation of data also requires the ability to combine data analysis with knowledge and expertise that is held by the interpreter. It is this combination of visual perception techniques to see the information, combined with interpreter guidance to understand what is seen, that makes interpretation of seismic data effective. Geological Expression workflows that are data driven and interpreter guided enable us to see and effectively interpret the geology that is present in the seismic data. In effect this gives us a Cognitive Interpretation of the data.
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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 (August 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 modeling and interpretation. To further reduce uncertainty in velocity model building and improve imaging quality, our geologists, geophysicists, and reservoir engineers worked closely through the exploration cycle (seismic acquisition, processing, and interpretation). This exploration model has been used successfully in hydrocarbon exploration of many complex foothill areas in western China. Three-dimensional WAZ high-density seismic surveys have been conducted over 40,000 km2 of the foreland basins, greatly improving the field seismic data quality. After application of 3D true-surface TTI anisotropic depth model building and imaging with integrated structural interpretation, new discoveries of hydrocarbon reservoirs have increased. The application of new technologies not only increased drilling success but also reduced depth well-tie errors between seismic data and wells.
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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 (January 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 (May 1, 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 technology provides value to reservoir characterization in shale gas exploration. We improved fault interpretations and natural fracture identifications by means of [Formula: see text] and [Formula: see text] integrated interpretation. In addition, using P-P-/P-SV-joint inversion, we extracted key parameters, such as [Formula: see text] ratio and density, that improve stratigraphic interpretation and rock-property descriptions of shale gas reservoirs.
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Wu, Xinming, Simon Luo, and Dave Hale. "Moving faults while unfaulting 3D seismic images." GEOPHYSICS 81, no. 2 (March 1, 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 both methods, we have used estimated fault positions and slip vectors to construct unfaulting equations for image samples alongside faults, and we have constructed simple partial differential equations for samples away from faults. We have solved these two different kinds of equations simultaneously to compute unfaulting vector shifts that are continuous everywhere except at faults. We have tested both methods on a synthetic seismic image containing normal, reverse, and intersecting faults. We also have applied one of the methods to a real 3D seismic image complicated by numerous intersecting faults.
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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 (February 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 for fracture characterization, found that the resulting diffraction image had lateral resolution comparable to or greater than the discontinuity-type attributes and provided information complementary to azimuthal anisotropy measurements. The diffraction image from Piceance Creek had advantages over attributes in interpretation confidence because diffractions were a direct seismic response to subsurface features of intermediate size. Although these features were larger than the fractures thought to influence production, knowledge of intermediate-scale features can improve fracture prediction in the context of geologic scaling relationships or rock physics models. Qualitative interpretation of the diffraction amplitudes distinguished edge-type and line-type diffractions, indicative of fault versus channel-fill features, respectively. Even the largest faults at Piceance Creek only generated diffractions where contrasting lithologies were juxtaposed. Where there was lateral contrast, diffractions appeared to delineate small faults and channels with vertical resolution limited to the same order as the conventional seismic image.
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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 (August 1, 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 ambiguity generally associated with designing NNs for seismic object detection can be reduced via statistical analysis of the extracted attribute data. A data-driven approach to selecting the appropriate set of input seismic attributes, as well as the locations and suggested number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized NN technique is demonstrated using 3D seismic reflection data collected from the southeastern portion of the Florida carbonate platform. Several dimensionality reduction methods are applied, and the resulting karst probability models are evaluated relative to one another based on quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, with previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multiattribute workflow presented is capable of approximating the structural boundaries of karst systems with more accuracy and efficiency than a human counterpart or previously presented seismic interpretation schemes. This objective technique, using solely 3D seismic reflection data, is proposed as a practical approach to mapping karst systems for subsequent hydrogeologic modeling.