Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

Zhao, Tao; Li, Fangyu; Marfurt, Kurt J.

doi:10.1190/geo2017-0192.1

Cited by 49 publications

(11 citation statements)

References 39 publications

(41 reference statements)

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“…Paleokarst caves and solution collapses are extensively generated in the Ellenburger group because it went through a series of karst events ranging between the post Ellenburger and Early Pennsylvanian (Canter et al, 1993). In addition, the strata of the Silurian and Devonian strata are missing in the two study areas, which provides additional time for generating paleokarst in the Ellenburger group (Sullivan et al, 2006; Zhao et al, 2018). Figure 4a shows a model (Kerans, 1988) of the paleokarst system and the associated developments in the Ellenburger group.…”

Section: Field Seismic Data and Geologic Backgroundmentioning

confidence: 99%

“…Automatic paleokarst interpretation based on such attributes alone, however, remains challenging because these attributes are typically sensitive to noise and other types of structural discontinuities or variations that are unrelated to the collapsed paleokarst features. Significant human interactions are still required to delineate the collapsed paleokarst systems by integrating multiple types of seismic attributes (Khatiwada et al, 2013; Qi et al, 2014; Sullivan et al, 2006; Zhao et al, 2018)…”

Section: Introductionmentioning

confidence: 99%

“…Buried, collapsed paleokarst systems can form carbonate petroleum reservoirs including those explored in the Fort Worth Basin (FWB) (e.g., Dou et al, 2011; Kerans, 1988; Loucks & Anderson, 1985), the Lower Cretaceous Golden Lane field in eastern Mexico (Coogan et al, 1972; Viniegra & Castillo‐Tejero, 1970), the Tarim Basin in China (e.g., Desheng et al, 1996; Maoshan et al, 2011; Qi et al, 2014; Zeng et al, 2011), and other paleokarst‐related fields reported in previous works (e.g., Andre & Doulcet, 1970; Loucks, 1999; Mazzullo & Mazzullo, 1970; Zhai & Zha, 1982). In addition, collapsed paleokarst systems pose potential drilling hazards due to the weakness of the paleokarst zones (Qi et al, 2014; Soriano et al, 2019; Zhao et al, 2018). Therefore, delineating subsurface collapsed paleokarst systems is important for petroleum reservoir characterization and production.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Yan

et al. 2020

JGR Solid Earth

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Paleokarst systems are found extensively in carbonate-prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3-D seismic images. To avoid time-consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3-D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN-based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN-based paleokarst characterization, we can further automatically extract 3-D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN-based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3-D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km 2) by using one graphics processing unit. We propose a convolutional neural network (CNN) to fully detect paleokarst collapse features in 3-D seismic images and extract 3-D paleokarst chimney tubes all at once, which would provide a clear 3-D perspective of

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Section: Field Seismic Data and Geologic Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Yan

et al. 2020

JGR Solid Earth

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“…Zhao et al . (2018) applied user‐guided unsupervised methods to select seismic attributes for facies analysis. Qi et al .…”

Section: Introductionmentioning

confidence: 99%

Semi‐supervised deep autoencoder for seismic facies classification

Liu

et al. 2021

Geophysical Prospecting

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Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir prediction. Facies identification based on supervised machine learning methods usually requires a large amount of labelled data, which are sometimes difficult to obtain. Here, we introduce the deep autoencoder to learn the hidden features and conduct facies classification from elastic attributes. Both labelled and unlabelled data are involved in the training process. Then, we develop a semi‐supervised deep autoencoder by taking the mean of intra‐class and the whole population of facies into account to construct a classification regularization term, thereby improving the classification accuracy and reducing the uncertainty. The new method inherits the profits of deep autoencoder and absorbs the information provided by labelled data. The proposed method performs well and produces promising results when it is used to address problems of reservoir prediction and facies identification. The new method is evaluated on both well and seismic data and compared with the conventional deep autoencoder method, which demonstrates its feasibility and superiority with respect to classification accuracy.

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“…. 탄성파 탐사 분야에서는 현재까지 탄성 파 상 분석 (Wrona et al, 2018;Keynejad et al, 2019), 저류 층 특성화 (Chaki, 2015;Tian and Daigle, 2018;Smith et al, 2019), 단층해석 (Zhang et al, 2014;Xiong et al, 2018;Kumar and Sain, 2018), 암염구조 해석 (Guillen et al, 2015;Shi, et al, 2019;Sen et al, 2019), 탄성파 속성분석 (Zhao, 2018b;Griffith et al, 2019) (Bengio et al, 1994;Glorot and Bengio, 2010…”

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Comparison of Convolutional Neural Networks for Dividing Seismic Sequences

Moon¹,

Jou

Lee

et al. 2020

ksmer

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Application of the machine learning technique is expanding to the field of seismic exploration. For the purpose of feasibility assessment, we applied the machine learning technique to seismic sequence identification, which is important in seismic interpretation. From among the convolutional neural network techniques used in image analysis, we applied four models to seismic data obtained in the F3 block, offshore Netherlands, which have yielded remarkable results. One of the four models was ResNet34. The others were encoderdecoder models: U-Net, Residual U-Net, and FD U-Net. We first performed a qualitative analysis of the predicted images and then conducted quantitative analysis using pixel accuracy, mean class accuracy, mean intersection over union, and frequency weighted IU equations. The numerical results showed that ResNet34 had the lowest accuracy and that the encoder-decoder type models had higher accuracy. Considering the number of parameters required for calculation and the learning time, we confirmed that U-Net is the most efficient model.

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Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

Cited by 49 publications

References 39 publications

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Semi‐supervised deep autoencoder for seismic facies classification

Comparison of Convolutional Neural Networks for Dividing Seismic Sequences

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