Unfaulting and unfolding 3D seismic images

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. O45-O56 ◽  
Author(s):  
Simon Luo ◽  
Dave Hale
Keyword(s):  
Structural Deformation ◽  
3D Seismic ◽  
Seismic Image ◽  
Seismic Images

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.

Detecting faults and channels while enhancing seismic structural and stratigraphic features

2019 ◽  
Vol 7 (1) ◽  
pp. T155-T166 ◽  
Author(s):  
Xinming Wu ◽  
Zhenwei Guo
Keyword(s):  
Iterative Scheme ◽  
3D Seismic ◽  
Linear Features ◽  
Smoothing Methods ◽  
Seismic Image ◽  
Diffusion Scheme ◽  
Seismic Reflections ◽  
2D And 3D ◽  
Seismic Images

A 3D seismic image contains structural and stratigraphic features such as reflections, faults, and channels. When smoothing such an image, we want to enhance all of these features so that they are easier to interpret. Most smoothing methods aim to enhance reflections but may blur faults and channels in the image. A few methods smooth seismic reflections while preserving faults and channel boundaries. However, it has not well-discussed to smooth simultaneously along the seismic reflections and channels, which are linear features apparent within dipping reflections. In addition, to interpret faults and channels, extra steps are required to compute attributes or mappings of faults and channels from a seismic image. Such fault and channel attributes are often sensitive to noise because they are typically computed as discontinuities of seismic reflections. In this paper, we have developed methods to simultaneously enhance seismic reflections, faults, and channels while obtaining mappings of the faults and channels. In these methods, we first estimate the orientations of the reflections, faults, and channels directly in a seismic image. We then use the estimated orientations to control the smoothing directions in an efficient iterative diffusion scheme to smooth a seismic image along the reflections and channels. In this iterative scheme, we also efficiently compute mappings of faults and channels, which are used to control smoothing extents in the diffusion to stop smoothing across them. This diffusion scheme iteratively smooths a seismic image along reflections and channels while updating the mappings of faults and channels. By doing this, we will finally obtain an enhanced seismic image (with enhanced reflections and channels and sharpened faults) and cleaned mappings of faults and channels (discontinuities related to noise are cleaned up). We have examined the methods using 2D and 3D real seismic images.

Edge-aware filtering with Siamese neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 711-717
Author(s):  
Mehdi Aharchaou ◽  
Michael Matheney ◽  
Joe Molyneux ◽  
Erik Neumann
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Learning Ability ◽  
Seismic Exploration ◽  
Data Sets ◽  
3D Seismic ◽  
New Class ◽  
Seismic Image ◽  
Siamese Networks ◽  
Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

Automatically interpreting all faults, unconformities, and horizons from 3D seismic images

2016 ◽  
Vol 4 (2) ◽  
pp. T227-T237 ◽  
Author(s):  
Xinming Wu ◽  
Dave Hale
Keyword(s):  
Image Processing ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Array Processing ◽  
3D Seismic ◽  
Partial Differential ◽  
Seismic Image ◽  
Processing Procedure ◽  
Seismic Images

Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although others automate the extraction of each type of these surfaces to some extent, it is difficult to automatically interpret a seismic image with all three types of surfaces because they could intersect with each other. For example, horizons can be especially difficult to extract from a seismic image complicated by faults and unconformities because a horizon surface can be dislocated at faults and terminated at unconformities. We have proposed a processing procedure to automatically extract all the faults, unconformities, and horizon surfaces from a 3D seismic image. In our processing, we first extracted fault surfaces, estimated fault slips, and undid the faulting in the seismic image. Then, we extracted unconformities from the unfaulted image with continuous reflectors across faults. Finally, we used the unconformities as constraints for image flattening and horizon extraction. Most of the processing was image processing or array processing and was achieved by efficiently solving partial differential equations. We used a 3D real example with faults and unconformities to demonstrate the entire image processing.

Moving faults while unfaulting 3D seismic images

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. IM25-IM33 ◽  
Author(s):  
Xinming Wu ◽  
Simon Luo ◽  
Dave Hale
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Extraction Methods ◽  
Seismic Interpretation ◽  
3D Seismic ◽  
Partial Differential ◽  
Multiple Faults ◽  
Seismic Image ◽  
Seismic Reflectors ◽  
Seismic Images

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.

3D seismic image processing for unconformities

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. IM35-IM44 ◽  
Author(s):  
Xinming Wu ◽  
Dave Hale
Keyword(s):  
Image Processing ◽  
3D Seismic ◽  
Amplitude Image ◽  
Tensor Method ◽  
Seismic Amplitude ◽  
Seismic Image ◽  
Automatic Methods ◽  
Normal Vectors ◽  
Seismic Reflectors ◽  
Seismic Images

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.

Seismic fault detection with convolutional neural network

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O97-O103 ◽  
Author(s):  
Wei Xiong ◽  
Xu Ji ◽  
Yue Ma ◽  
Yuxiang Wang ◽  
Nasher M. AlBinHassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Field Data ◽  
Data Sets ◽  
3D Seismic ◽  
Learning Tools ◽  
Seismic Fault ◽  
Seismic Image ◽  
Coherence Estimation ◽  
Seismic Images

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

ChannelSeg3D: channel simulation and deep learning for channel interpretation in 3D seismic images

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Hang Gao ◽  
Xinming Wu ◽  
Guofeng Liu
Keyword(s):  
Seismic Data ◽  
Structural Deformation ◽  
3D Seismic ◽  
Channel Models ◽  
Meandering Channel ◽  
Seismic Discontinuities ◽  
Seismic Volumes ◽  
Seismic Images ◽  
Channel Structures

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 propose a convolutional neural network (CNN) method to improve the automatic channel interpretation. The key problem in applying the CNNs method into channel interpretation is the absence of the labeled field seismic images for training the CNNs. To solve this problem, we propose a workflow to automatically generate numerous synthetic training datasets with realistic channel structures. In this workflow, we first randomly simulate various meandering channel models based on geological 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 which learns the characterization of channel structures. Although trained on only synthetic seismic volumes, this CNN shows an 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.

Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. IM119-IM126 ◽  
Author(s):  
Xinming Wu
Keyword(s):  
3D Seismic ◽  
Human Interactions ◽  
Likelihood Image ◽  
Seismic Image ◽  
Seismic Images

Salt body interpretation is important for building subsurface models and interpreting seismic horizons and faults that might be truncated by the salt. Salt interpretation often includes two steps: highlighting salt boundaries with a salt attribute image and extracting salt boundaries from the attribute image. Although both steps have been automated to some extent, salt interpretation today typically still requires significant manual effort. From a 3D seismic image, I first efficiently compute a salt likelihood image, in which the ridges of likelihood values indicate locations of salt boundaries. I then extract salt samples on the ridges, and these samples can be directly connected to construct salt boundaries in cases when salt structures are simple and the boundaries are clean. In more complicated cases, these samples may be noisy and incomplete, and some of the samples can be outliers unrelated to salt boundaries. Therefore, I have developed a method to accurately fit noisy salt samples, reasonably fill gaps, and handle outliers to simultaneously construct multiple salt boundaries. In this step of constructing salt boundaries, I also have developed a convenient way to incorporate human interactions to obtain more accurate salt boundaries in especially complicated cases. I have performed the methods of computing salt likelihoods and constructing salt surfaces using a 3D seismic image containing multiple salt bodies.

Pitfall experiences when interpreting complex structure with low-quality seismic images

2015 ◽  
Vol 3 (1) ◽  
pp. SB29-SB37 ◽  
Author(s):  
Bob A. Hardage
Keyword(s):  
Seismic Data ◽  
Structural Model ◽  
Signal To Noise Ratio ◽  
Complex Structure ◽  
Lessons Learned ◽  
Structural Deformation ◽  
Structural Interpretation ◽  
Seismic Image ◽  
Data Volume ◽  
Seismic Images

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.

3D seismic image processing for faults

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. IM1-IM11 ◽  
Author(s):  
Xinming Wu ◽  
Dave Hale
Keyword(s):  
Image Processing ◽  
Data Structure ◽  
Linked Data ◽  
Difficult Problem ◽  
Fault Slip ◽  
3D Seismic ◽  
Fault Surface ◽  
Subsequent Processing ◽  
Seismic Image ◽  
Seismic Images

Numerous methods have been proposed to automatically extract fault surfaces from 3D seismic images, and those surfaces are often represented by meshes of triangles or quadrilaterals. However, extraction of intersecting faults is still a difficult problem that is not well addressed. Moreover, mesh data structures are more complex than the arrays used to represent seismic images, and they are more complex than necessary for subsequent processing tasks, such as that of automatically estimating fault slip vectors. We have represented a fault surface using a simpler linked data structure, in which each sample of a fault corresponded to exactly one seismic image sample, and the fault samples were linked above and below in the fault dip directions, and left and right in the fault strike directions. This linked data structure was easy to exchange between computers and facilitated subsequent image processing for faults. We then developed a method to construct complete fault surfaces without holes using this simple data structure and to extract multiple intersecting fault surfaces from 3D seismic images. Finally, we used the same structure in subsequent processing to estimate fault slip vectors and to assess the accuracy of estimated slips by unfaulting the seismic images.

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