The Experts below are selected from a list of 7902 Experts worldwide ranked by ideXlab platform
Yaojun Wang - One of the best experts on this subject based on the ideXlab platform.
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unsupervised seismic Facies Analysis via deep convolutional autoencoders
Geophysics, 2018Co-Authors: Feng Qian, Yaojun Wang, Cai Lu, Guangmin HuAbstract:ABSTRACTOne of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic Facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate Facies maps. Therefore, it is promising to use prestack seismic data for the seismic Facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data-driven offset-temporal feature extraction approach using the deep convolutional autoencoder (DCAE). As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic Facies Analysis can be accomplished through the use of conventional classification or clustering techniques (e.g., K-means or self-organizing maps). Using a physical model and field prestack seismic surveys, we c...
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Unsupervised seismic Facies Analysis with spatial constraints using regularized fuzzy c-means
Journal of Geophysics and Engineering, 2017Co-Authors: Chengyun Song, Zhining Liu, Hanpeng Cai, Yaojun WangAbstract:Seismic Facies Analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic Facies Analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear Facies boundaries and channel morphology, which reveals that the method is an effective seismic Facies Analysis tool.
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Multi-waveform classification for seismic Facies Analysis
Computers & Geosciences, 2017Co-Authors: Chengyun Song, Zhining Liu, Yaojun WangAbstract:Seismic Facies Analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic Facies, which does not consider the stratigraphy continuity, and the final Facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic Facies Analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of Facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic Facies Analysis. Classifying the multi-waveform in a 3D window can suppress the effect of data noise.Multi-window dip search algorithm is used to extract multi-waveform.Multilinear subspace learning is used to reduce dimension of multi-waveform.
Chengyun Song - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised seismic Facies Analysis with spatial constraints using regularized fuzzy c-means
Journal of Geophysics and Engineering, 2017Co-Authors: Chengyun Song, Zhining Liu, Hanpeng Cai, Yaojun WangAbstract:Seismic Facies Analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic Facies Analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear Facies boundaries and channel morphology, which reveals that the method is an effective seismic Facies Analysis tool.
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Multi-waveform classification for seismic Facies Analysis
Computers & Geosciences, 2017Co-Authors: Chengyun Song, Zhining Liu, Yaojun WangAbstract:Seismic Facies Analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic Facies, which does not consider the stratigraphy continuity, and the final Facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic Facies Analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of Facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic Facies Analysis. Classifying the multi-waveform in a 3D window can suppress the effect of data noise.Multi-window dip search algorithm is used to extract multi-waveform.Multilinear subspace learning is used to reduce dimension of multi-waveform.
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Pre-stack-texture-based reservoir characteristics and seismic Facies Analysis
Applied Geophysics, 2016Co-Authors: Chengyun Song, Zhining Liu, Hanpeng Cai, Feng QianAbstract:Seismic texture attributes are closely related to seismic Facies and reservoir characteristics and are thus widely used in seismic data interpretation. However, information is mislaid in the stacking process when traditional texture attributes are extracted from post-stack data, which is detrimental to complex reservoir description. In this study, pre-stack texture attributes are introduced, these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset, anisotropy, and heterogeneity in the medium. Due to its strong ability to represent stratigraphics, a pre-stack-data-based seismic Facies Analysis method is proposed using the self-organizing map algorithm. This method is tested on wide azimuth seismic data from China, and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified, in addition to the method’s ability to reveal anisotropy and heterogeneity characteristics. The pre-stack texture classification results effectively distinguish different seismic reflection patterns, thereby providing reliable evidence for use in seismic Facies Analysis.
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Prestack Reflection Pattern Based Seismic Facies Analysis
SEG Technical Program Expanded Abstracts 2015, 2015Co-Authors: Chengyun Song, Zhining LiuAbstract:Seismic Facies Analysis provides an effective way to estimate the properties of reservoir and characterize its heterogeneity. The conventional approach used to generate a map of seismic Facies utilizes the poststack seismic attributes. In order to find more complex and atypical reservoirs, it is increasing essential to analyse the reservoir based on prestack seismic data which carries abundant stratigraphic and depositional information. In this paper, we develop a prestack reflection pattern based seismic Facies Analysis methodology. We extract an augmented Gabor feature derived from the Gabor wavelet representation of prestack data. Then, combining the feature with pattern recognition techniques, seismic Facies Analysis can be achieved through unsupervised or supervised learning. We tested the effectiveness of our method with application to the wide-azimuth data form LZB region and the CMP gather data from Sulige region, The results are geologically intriguing, and they demonstrate the vast superiority over the conventional approach.
Kurt J. Marfurt - One of the best experts on this subject based on the ideXlab platform.
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Seismic attribute selection for machine-learning-based Facies Analysis
GEOPHYSICS, 2020Co-Authors: Bo Zhang, Bin Lyu, Kurt J. MarfurtAbstract:Interpreters face two main challenges in seismic Facies Analysis. The first challenge is to define, or “label,” the Facies of interest. The second challenge is to select a suite of attributes that can differentiate a target Facies from the background reflectivity. Our key objective is to determine which seismic attributes can best differentiate one class of chaotic seismic Facies from another using modern machine-learning technology. Although simple 1D histograms provide a list of candidate attributes, they do not provide insight into the optimum number or combination of attributes. To address this limitation, we have conducted an exhaustive search whereby we represent the target and background training Facies by high-dimensional Gaussian mixture models (GMMs) for each potential attribute combination. The first step is to choose candidate attributes that may be able to differentiate chaotic mass-transport deposits and salt diapirs from the more conformal, coherent background reflectors. The second step is to draw polygons around the target and background Facies to provide the labeled data to be represented by GMMs. Maximizing the distance between all GMM Facies pairs provides the optimum number and combination of attributes. We use generative topographic mapping to represent the high-dimensional attribute data by a lower dimensional 2D manifold. Each labeled Facies provides a probability density function on the manifold that can be compared to the probability density function of each voxel, providing the likelihood that a given voxel is a member of each of the Facies. Our first example maps chaotic seismic Facies associated with the development of salt diapirs and minibasins. Our second example successfully delineates karst collapse underlying a shale resource play from north Texas.
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Seismic attribute selection for unsupervised seismic Facies Analysis using user guided data-adaptive weights
GEOPHYSICS, 2018Co-Authors: Tao Zhao, Kurt J. MarfurtAbstract:With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition driven seismic Facies Analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute Facies Analysis based on their experience and geologic target of interest. However, such an approach may overlook unsuspected features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of which candidate attributes best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the Facies map, we weight the interpreter-selected input attributes based on both their response from the unsupervised learning algorithm and interpreter’s knowledge. In other...
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constraining self organizing map Facies Analysis with stratigraphy an approach to increase the credibility in automatic seismic Facies classification
Interpretation, 2017Co-Authors: Tao Zhao, Fangyu Li, Kurt J. MarfurtAbstract:AbstractPattern recognition-based seismic Facies Analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps (SOMs) as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical Facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute Facies Analysis begins by correlating a suite of candidate attributes to a desired output, keeping those that correlate best for subsequent Analysis. The Analysis then takes place in attribute space rather than (x, y, and z) space, removing spatial trends often observed by interpreters. We add a stratigraphy layering component to a SOM model that attempts to preserve the intersample relation along the vertical axis. Specifically, we use a mode decompo...
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Seismic Facies Analysis and Age Dating of Mid-Pleistocene Channel-Lobe Deposits, Mad Dog Field, Gulf of Mexico
2015Co-Authors: Oluwayomi A. Oyedele, William R. Dupre, Kurt J. MarfurtAbstract:Abstract The study area is located within the Mad Dog development area in the Gulf of Mexico. Several studies have been carried out in the area, including the use of coherence and seismic amplitudes to identify fan sands behind the prominent Sigsbee Escarpment. This paper focuses on integrating seismic attributes and age dates to infer the depositional history of one of the fan systems landward of the escarpment, and is based on data sets licensed to the University of Houston by BP America, Inc. The detailed architectural and Facies Analysis of the fan system (a channel-lobe complex) was interpreted using 3D high-resolution seismic imaging and seismic attributes. Integration of the seismic Facies Analysis with biostratigraphic ages was used to infer the prevalent geologic processes and thus the depositional history of the system. The study revealed the channel-lobe complex to consist of multiple smaller splays, the youngest of which is very similar to a turbulent jet plume model based on flume studies. Deposition was mainly controlled by the interaction of changing sea level, sedimentation rates, and salt movement. Age dates of bounding markers reveal that the channel-lobe complex was probably initiated and deposited during a period of lowstand to rising sea level (oxygen-isotope stages 14 and 13).
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Seismic Facies Analysis using generative topographic mapping
SEG Technical Program Expanded Abstracts 2014, 2014Co-Authors: Satinder Chopra, Kurt J. MarfurtAbstract:Summary Seismic Facies Analysis is commonly carried out by classifying seismic waveforms based on their shapes in an interval of interest. It is also carried out by using different seismic attributes, reducing the dimensionality of the input data volumes using Kohonen’s self-organizing maps (SOM), and organizing it into clusters on a 2D map. Such methods are computationally fast and inexpensive. However, they have shortcomings in that there is no definite criteria for selection of a search radius and the learning rate, as these are parameters dependent on the input data. In addition, there is no cost function that is defined and optimized and so usually the method is deficient in providing a measure of confidence that could be assigned to the results. Generative topographic mapping (GTM) has been shown to address the shortcomings of the SOM method and suggested as an alternative to it. We demonstrate the application of GTM to a dataset from central Alberta, Canada and show that its performance is more encouraging than the simplistic waveform classification or the SOM multiattribute approach.
Zhining Liu - One of the best experts on this subject based on the ideXlab platform.
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Pre-stack Seismic Facies Analysis via Waveform Sparse Representation
GEOPHYSICS, 2020Co-Authors: Zhining Liu, Bin She, Jiandong LiangAbstract:Seismic Facies Analysis based on pre-stack data is becoming popular. Vertical elastic transitions produce the spatial structure variation of pre-stack waveforms, while lateral elastic transitions produce the amplitude intensity variation. In the stratigraphic seismic Facies Analysis, more attention should be paid to waveform spatial structure than amplitude intensity. Conventional classification methods based on distance metric are difficult to adapt to stratigraphic seismic Facies Analysis because a distance metric is a comprehensive measure of waveform structure and amplitude intensity. A dictionary learning method for pre-stack seismic Facies Analysis is proposed herein. The proposed method first learns several dictionaries from labeled pre-stack waveform data, and these dictionaries consist of several normalization vector bases. The pre-stack waveform spatial structure is therefore embedded in these learned dictionaries, and the amplitude intensity is eliminated by the normalization process. Afterward, these dictionaries are used to sparsely represent pre-stack seismic data. Seismic Facies are classified and determined according to representation error. A source error separation method is used to improve the anti-noise performance of dictionary learning by iteratively segmenting the noise out in the training data. The results on synthetic and real seismic data show that the proposed method has a stronger tolerance to noise, and the obtained seismic Facies boundary is more accurate and clearer. This demonstrates that the proposed method is an effective seismic Facies Analysis technique.
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Unsupervised seismic Facies Analysis with spatial constraints using regularized fuzzy c-means
Journal of Geophysics and Engineering, 2017Co-Authors: Chengyun Song, Zhining Liu, Hanpeng Cai, Yaojun WangAbstract:Seismic Facies Analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic Facies Analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear Facies boundaries and channel morphology, which reveals that the method is an effective seismic Facies Analysis tool.
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Multi-waveform classification for seismic Facies Analysis
Computers & Geosciences, 2017Co-Authors: Chengyun Song, Zhining Liu, Yaojun WangAbstract:Seismic Facies Analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic Facies, which does not consider the stratigraphy continuity, and the final Facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic Facies Analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of Facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic Facies Analysis. Classifying the multi-waveform in a 3D window can suppress the effect of data noise.Multi-window dip search algorithm is used to extract multi-waveform.Multilinear subspace learning is used to reduce dimension of multi-waveform.
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Pre-stack-texture-based reservoir characteristics and seismic Facies Analysis
Applied Geophysics, 2016Co-Authors: Chengyun Song, Zhining Liu, Hanpeng Cai, Feng QianAbstract:Seismic texture attributes are closely related to seismic Facies and reservoir characteristics and are thus widely used in seismic data interpretation. However, information is mislaid in the stacking process when traditional texture attributes are extracted from post-stack data, which is detrimental to complex reservoir description. In this study, pre-stack texture attributes are introduced, these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset, anisotropy, and heterogeneity in the medium. Due to its strong ability to represent stratigraphics, a pre-stack-data-based seismic Facies Analysis method is proposed using the self-organizing map algorithm. This method is tested on wide azimuth seismic data from China, and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified, in addition to the method’s ability to reveal anisotropy and heterogeneity characteristics. The pre-stack texture classification results effectively distinguish different seismic reflection patterns, thereby providing reliable evidence for use in seismic Facies Analysis.
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Prestack Reflection Pattern Based Seismic Facies Analysis
SEG Technical Program Expanded Abstracts 2015, 2015Co-Authors: Chengyun Song, Zhining LiuAbstract:Seismic Facies Analysis provides an effective way to estimate the properties of reservoir and characterize its heterogeneity. The conventional approach used to generate a map of seismic Facies utilizes the poststack seismic attributes. In order to find more complex and atypical reservoirs, it is increasing essential to analyse the reservoir based on prestack seismic data which carries abundant stratigraphic and depositional information. In this paper, we develop a prestack reflection pattern based seismic Facies Analysis methodology. We extract an augmented Gabor feature derived from the Gabor wavelet representation of prestack data. Then, combining the feature with pattern recognition techniques, seismic Facies Analysis can be achieved through unsupervised or supervised learning. We tested the effectiveness of our method with application to the wide-azimuth data form LZB region and the CMP gather data from Sulige region, The results are geologically intriguing, and they demonstrate the vast superiority over the conventional approach.
Haoran Zhang - One of the best experts on this subject based on the ideXlab platform.
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Seismic Facies Analysis Based on Deep Learning
IEEE Geoscience and Remote Sensing Letters, 2020Co-Authors: Yuxi Zhang, Haoran ZhangAbstract:Seismic Facies Analysis is to study the sedimentary environment of stratigraphic sequence and provides an important basis for reservoir prediction. Most of the existing Analysis methods have low efficiency and heavily rely on manual experience, and therefore, it is difficult to interpret increasingly complex seismic data. Deep learning techniques can help to solve these problems and achieve automatic seismic Facies classification. We regard seismic Facies classification as a target segmentation problem and propose new method and training strategies. Our workflow primarily involves four sections. First, we process the manually annotated labels and seismic data with mirroring and cropping operations to ensure that network can accept input with arbitrary size and the model training is not limited to GPU memory. Second, data augmentation is applied to automatically generate massive training samples from the processed data. Third, we build two independent networks based on encoder–decoder architecture: one identifies all seismic Facies simultaneously, and the other identifies single seismic Facies in each model. However, both the results of the two networks have some drawbacks. Fourth, to overcome these drawbacks, we propose an ensemble learning method to get optimized model and test it on 3-D seismic data. The testing results manifest that the proposed method can improve the predictive ability of model, accurately describe the seismic Facies, and can be applicable to entire seismic data volume.
