Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration. The main cost of seismic exploration is acquiring seismic data, which can be significantly reduced through compressed sensing (CS) techniques. Traditional and deep learning (DL) CS methods offer unprecedented opportunities for cost optimization while maintaining data fidelity. However, CS methods rely on random acquisition, which performs poorly when the seismic data are not randomly acquired. This manuscript proposes a novel physics-informed neural network (PINN) framework for reconstructing 3D seismic data acquired via down-sampling from Ocean Bottom Seismometer (OBS) observation systems. The compressed sensing acquisition system of seismic data contains two types of sparsity: 1) 2D random missing traces, 2) Dual random missing of source lines and source points. The proposed method employed move-out (MO) transformations with multiple constant velocities to mitigate aliasing artifacts and improve reconstruction accuracy. Then, a pre-interpolation process is utilized for the MO-transformed seismic data groups. Additionally, a semblance evaluation mechanism dynamically assigns weights to each MO dataset, generating optimized, pre-interpolated seismic profiles. Finally, the PINN architecture integrates physical constraints to refine the reconstructed data. The experimental results demonstrate the superior reconstruction performance and computational efficiency of the proposed method compared with the state-of-the-art.

Convolutional neural networks (CNNs) have been widely employed for seismic fault segmentation and show more powerful performance than conventional attribute-based methods to obtain a fault map with noise-free and continuously trackable fault features. However, CNN-based methods face the potential problem of poor generalization in field seismic images and factors affecting the fault segmentation remains incompletely studied or unexplored. Moreover, the existing pixel-wise metrics, borrowed from the natural image segmentation tasks, cannot fairly or reasonably evaluate the fault segmentation results.We firstly propose to use a distance-based metric to provide a geologically more reasonable evaluation on fault interpretation. We then use the most commonly used U-net architecture as an example to study how the CNN-based fault segmentation is affected by some significant factors such as training data, all kinds of network hyperparameters, and scaling and rotation in the inference step. Experimental results show that a training dataset with more realistic reflection features and multiple sampling rates can significantly enhance the fault segmentation.Besides, a novel loss function we proposed outperforms others with notable margins. Last but not least, it is necessary to merge predictions with multiple scales and rotations in the reference step because the CNN does not preserve transformation invariance. Based on the studies, we optimally train a properly designed CNN and apply it to multiple field examples, where we obtain accurate, clean, continuous fault detections and quantitatively evaluate them with manual interpretations.

The task of general first arrival picking is challenging due to varying signal-to-noise ratios in considering both onshore and offshore environments. Deep learning-based picking methods show a successful usage in the field seismic data. However, the limited and unbalanced quantity of high-quality labels constrain the application of supervised deep learning techniques. In this paper, we propose an data enhancement method based on a conditional generative adversarial network (Pix2pix), prior to the training of seismic wave first arrival picking to produce seismic data and corresponding labeled data infinitely. The results test on a combination of onshore and offshore seismic data prove the effectiveness of our method. Enhanced method shows a high accurate of first arrival picking and the results are more resistant to interference from strong amplitude signals.

Wave separation is crucial for enhancing seismic resolution. Conventional techniques, which rely on distinct wave characteristics, can be complex, subjective, and inconsistently reproducible. Deep learning has shown promise in wave separation tasks such as multiples suppression and P/S wave separation. However, the lack of understanding of how deep neural networks (DNNs) perform wave separation limits improvements in accuracy and robustness. We introduce an explainable AI technique to clarify DNN’s separation process by examining its middle layers. Our method, using back-propagation, generates saliency maps that show each DNN layer captures specific seismic attributes like slopes and amplitudes. Two case studies demonstrate that DNNs trained for wave separation can effectively learn first arrival picking.

Fault is one of the key elements in the exploration and development of oil and gas reservoirs. The accurate prediction of faults is related to the accurate evaluation of oil and gas migration, reservoir prediction, and other work, which is of great significance. With the development of artificial intelligence technology, fault automatic detection technology based on deep learning algorithms has developed rapidly. In order to solve the problems of weak generalization ability and poor fault continuity of conventional deep learning algorithms for seismic data with different sampling rates, a new multi-scale prediction and fusion enhancement process has been proposed. Firstly, down sampling the input 3D seismic data on three dimensions is performed, and then fault detection is performed on the original data and its corresponding seismic data with three different scales. Subsequently, the prediction results from down sampling in three different directions are resampled back to the original scale, and the fusion results of the four fault predictions are obtained by taking the highest value. Finally, a fault scanning enhancement method based on matched filtering is applied to scan the fusion results along their possible fault strike and dip angles, suppress noise in fault prediction and improve the continuity of fault prediction, obtaining high signal-to-noise ratio and high continuity fault enhancement results. The field data results show that this method has obvious advantages in noise resistance, accuracy, and efficiency compared to traditional methods, and has broad application prospects in faults interpretation, development well position adjustment, and other work.

The seismic data acquisition is an indispensable step in seismic exploration, whose cost takes up a large proportion of seismic exploration. The cost of seismic data acquisition has limited the development of industrial manufacturing. The compressed sensing (CS) method can obtain high-quality seismic data with less random sampling. Recently, deep learning (DL) based CS methods have achieved outstanding performance in the reconstruction of seismic data with randomly missing traces. However, most existing DL-based methods focus on the 2-D seismic data. The obstacle to applying DL to the reconstruction of 3-D seismic data is the lack of high-quality training data. Self-supervised learning can overcome the lack of high-quality training data. Nevertheless, the time cost is the biggest obstacle preventing the application of self-supervised learning methods. To solve the above issues, we propose a fast self-supervised learning method for the reconstruction of 3-D seismic data. The proposed method learns from the observed seismic data directly by subsampling. In addition, the 3-D lightweight gated convolution layers are utilized for highly efficient reconstruction of the input seismic data with randomly missing traces. Meanwhile, the proposed method employs a global waveform extractor based on a fast Fourier transform to extract global waveform. The synthetic and field experiments have demonstrated that the proposed method has a remarkable reconstruction performance with high efficiency.

我国大多数盆地都发育古河道沉积,现有研究表明古河道沉积已成为我国油气聚集的重要场所,对其进行可靠地预测对寻找古河道类型油气藏具有重要意思.利用地震属性来进行古河道的预测是目前的主要方法,但通过正演模拟研究发现,不同单一地震属性对河道沉积的敏感性不同,有些属性对大多数河道沉积的沉积比较敏感(如均方根振幅、平均反射强度等),有些对部分较厚的河道沉积特别敏感,而对其他河道沉积反映一般.基于这一认识,提出了一种称之为一主双次三属性组合的新属性,该属性结合了均方根属性、振幅的立方差和平均频率的优点而建立,S油田的正演模拟和应用表明,该组合属性相对单一敏感属性在预测古河道沉积方面更有优势.

The Permian Shihezi Formation is located at the LX block at the eastern margin of the Ordos Basin, and it develops tight sandstone reservoirs with fluvial facies. Reservoirs with high gas production feature a porosity of larger than 12%, a permeability of higher than 1 mD, and a gas saturation of more than 50%, and the quantitative evaluation of reservoir parameters shall be urgently carried out to find sweet spots with high production. However, the accuracy of indirectly predicting porosity and other parameters by traditional seismic inversion is low. In addition, the seismic data and well-logging curves of the LX block have inconsistent corresponding relations, and a lot of conflict samples exist, which makes conventional convolutional neural networks difficult to be applied. Therefore, a fully connected network architecture is added to the conventional convolutional neural network, and the seismic data and well-logging data are connected through local Toeplitz network architecture, so as to deal with the indirect correlation between reservoir parameters and seismic data. The fully connected network architecture can address the conflict samples by introducing prior information including the line/channel number, horizon, and seismic facies. Furthermore, a deep learning network model suitable for tight reservoirs is established by introducing prior constraint information such as stratigraphic framework and seismic facies, and a geo-oriented method for selecting the best sample well is developed, so as to quantitatively predict reservoir parameters and describe the plane distribution of the sweet spots in reservoirs with high gas production. The actual application results show that the predicted results of porosity, permeability, and gas saturation are in good agreement with the well-logging data, and the newly deployed five wells are tested and achieve an open-flow capacity of more than 10,000 m~3/d during drilling, which effectively promotes the efficient development of tight gas.

The time–frequency (TF) analysis is an effective tool in seismic signal processing. The sparsity-based TF transforms have been widely used to obtain high localized TF representations in recent past years. These TF transforms formulate a sparse TF representation as an inverse optimization problem using simple mathematical models, which are typically based on a handcrafted prior knowledge. Unlike the traditional sparsity-based TF transforms, the supervised deep learning (DL)-based sparse TF representations do not require this prior knowledge and instead use a large amount of labeled dataset, which is difficult to label for seismic data. In this study, to bridge the gap between the traditional sparsity-based transforms and the supervised DL-based transforms, we propose a DL-based sparse TF analysis approach based on a physically informed autoencoder model, named the SparseTFNet. The proposed SparseTFNet includes two modules: a convolutional neural networks (CNN)-based encoder and a traditional inverse TF representation-based decoder. The CNN-based encoder is implemented by training the inverse optimization problem in the absence of the “ground-truth” TF representation, which can be trained with only seismic traces. The traditional inverse short-time Fourier transform (STFT) is utilized as the decoder module in this study, which is used as a physical constraint to ensure the high accuracy of the calculated TF representation. Finally, after training and validating the proposed model using the noise-free and noisy synthetic seismic traces, the model is applied to 3-D offshore seismic data. The results show that the proposed SparseTFNet model has good performance in the delineation of the depositional fluvial channels.

断层解释的精度和效率对油气藏的勘探与开发非常重要.传统的断层解释方法多以人工为主,其依赖解释人员的经验且耗时较长;常规自动断层解释方法主要是分析地震数据的不连续性,往往涉及多个参数,因而断层解释精度多依赖选取的参数.近年来,随着深度学习技术的发展,非线性卷积神经网络能够描述地震数据中的不连续特征.为此,引入深度学习中的边缘检测技术,即整体嵌套边缘检测(Holistically-Nested Edge De-tection,HED)网络,并根据地震数据和断层特点对网络结构进行改进和优化,提出适用于地震断层智能解释的改进HED(Improved HED,IHED)网络.主要步骤包括:①将原始二维HED网络推广至三维,搭建三维HED网络;②根据HED网络的多尺度特点,调整三维HED网络构架;③利用三维合成地震数据及其标签数据训练得到三维IHED模型,将该模型用于实际地震数据进行断层智能解释.与相干体算法和U-Net模型相比,三维IHED模型对断层预测的准确性更高,连续性更好.该方法为地震断层智能识别提供了一条可靠途径.

Seismic acquisition guided by the compressive sensing theory can significantly improve seismic data acquisition efficiency and reduce the cost. After reviewing the basic principles of compressive sensing, we propose an optimized random sampling method that can control the maximum sampling interval and improve the design flexibility. We analyze several factors that can introduce reconstruction errors from compressive sensed data and learn that besides sampling method, reconstruction errors increase with decimation degree and the complexity of structures and also depend on the reconstruction workflow. In addition, we provide a basic workflow of the geometry design of compressive sensing acquisition. We analyze the feasibility of the three types of receiving equipment that are widely used in marine environment and discuss the potential cost reduction and efficiency gain. Our field example demonstrates the detailed working process and the feasibility of the combination of random sailing line intervals and random shot intervals and verifies the effect of cost saving and efficiency increasing.

Seismic full-waveform inversion (FWI) is able to build high-resolution velocity model based on the full information carried by seismic wave. However, FWI requires an accurate enough initial model to ensure convergence. In this article, we propose a new nonlinear FWI method to mitigate the initial model dependence problem. Specifically, we first propose a nonlinear operator within the hybrid model- and data-driven framework based on the frequency controllable envelope operator (FCEO) and a deep learning (DL) architecture U-Net. FCEO is used to obtain the envelope of a band-limited data and U-Net realizes the mapping from this envelope to that corresponding to a lower frequency band. The U-Net is trained in a self-supervised manner that avoids the reliance on labeled data and benefits the generalization ability. Based on the nonlinear operator, a nonlinear FWI method is proposed by defining a new misfit function. In addition, the calculation of gradient is derived using the adjoint state method. Using numerical examples, we investigate the performance of the proposed nonlinear operator and the new nonlinear FWI method. The results clearly demonstrate that the proposed nonlinear operator is effective in obtaining low-frequency envelope data, and the new nonlinear FWI method has advantages over common method in mitigating cycle-skipping and building an initial model for conventional FWI.

三维地震构造解释与建模是油气勘探开发的关键步骤之一,随着三维地震数据体的规模不断增大,大量依赖于人工的传统方法在效率、精度和分辨率方面均难以满足生产需求;同时,随着计算机软硬件技术的发展,基于计算机辅助的自动化三维地震构造解释与建模是必然趋势,并且近10年来该领域取得了较大进展.介绍并讨论了一整套全自动三维地震构造解释与建模的计算机实现技术流程及其在多个实际数据中的成功应用案例.该流程主要包括:①三维地震断层检测、断层面构建、断距场估计和断层恢复等一系列断层解释功能的实现;②盐丘、火成岩和溶洞等各类地质体的识别与三维建模;③不整合面、层序界面检测与提取;④基于断层、地质体和不整合面等边界信息约束的层位体解释和Wheeler体构建;⑤融合所有构造和层位解释结果的构造建模和井震联合物性参数建模.对相关方法技术进行了综述,并将其与相应的实际地震数据应用情况相结合展开讨论,以呈现整个自动化地震构造解释与建模过程中所面临的计算机技术问题及其实现情况.其中,断层检测、地质体识别和层位提取等问题得到了较好的自动化实现,而断层面组合、构造恢复、精细层序解释和构造建模等方面依然高度依赖人工参与.深度学习方法对所有这些任务的自动化实现均具有较好的应用前景,但目前仍需要更好地解决训练样本缺乏的问题以及如何合理引入地质、物理先验信息约束等方面的问题.同时,由于缺乏对结果的合理评价、质控和使用的友好度,自动化方法可能会面临在实际场景应用中未被合理使用或获得不合理结果的风险.但是,在自动化智能化发展的大背景驱使下,计算构造解释与建模的发展前景令人期待.

Delineating seismic faults is one of the main steps in seismic structure interpretation. Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For the DL-based models, there are two widely used techniques, which can enhance the model performance, that is, data augmentation (DA) and ensemble learning (EL). Qualitatively and quantificationally analyzing the performances of these two techniques is a rarely studied domain. In this study, we make detailed comparisons between the DL models using DA and EL. For the DL model with DA, we first build a holistically nested Unet (HUnet) model by adopting the holistically nested module to the widely used Unet model. Then, we train a HUnet model by using the original and its augmented synthetic datasets (HUnet-D model for short). Besides, we train a Unet model in the same way as a comparison (Unet-D model for short). On the other hand, for the DL model with EL, we first obtain several individual HUnet models separately trained by only using a type of the augmented datasets for each time. Next, we propose a data-driven EL model to integrate these HUnet models. Specially, we propose an adjoint-net module for the EL model to extract the multi-scale features from seismic data, which benefits for checking and fine-tuning the fusing results. Finally, we qualitatively and quantificationally evaluate these DL models (Unet-D, HUnet-D, and EL-HUnet) using the synthetic validation dataset. Moreover, we apply these models to 3-D field data volumes for automatic fault interpretation. Compared with the coherence attribute, Unet-D and HUnet-D models, we find that the EL-HUnet model achieves the comparable model performance for effectively enhancing the precision and continuity of the detected faults.

横波速度是反映储层岩性特征的重要参数之一,而常规测井数据中往往缺少横波速度数据.为此,根据横波速度与其他参数的关系,构建了端到端二次型寻优网络,利用 自然伽马、孔隙度和纵波速度直接预测横波速度,无需求解中间的过渡参数.网络训练过程使用二次型寻优算法替代Adam算法,同时采用正交试验法分析训练策略(包括优化算法)及不同参数(网络层数、训练井数等)对横波速度预测结果的影响.分析结果表明:优化算法对预测效果的影响最大,二次型寻优算法比Adam优化算法预测效果更好、效率更高;选择合适的激活函数可对预测效果起到积极的作用.根据正交试验结果,选择了最优的训练策略及网络参数进行横波速度预测.测试井的验证结果表明,该方法能够准确、有效地预测横波速度.

致密储层具有地层薄、孔隙度低、横向非均质性强等特点.现有储层预测技术在解决此类问题时,主要依靠人工从反演属性体中寻找可能的甜点区域.由于地层的砂岩含量、孔隙度值与地震反射特征并无直接关系,导致甜点识别准确率低.为此,根据测井数据和地震数据的空间分布特征和数据分布特征,将全局和局部连接网络相结合,有针对性地创建了适用于致密储层甜点预测的混合深度学习网络结构,其中局部连接网络负责学习数据分布特征,全局连接网络负责学习空间分布特征.在甜点预测时,先预测砂岩储层,在此基础上预测孔隙度值.为解决孔隙度数据分布不均匀、有效值与背景值比例不均衡的问题,以砂岩含量曲线为约束条件设置阈值,筛选高于阈值的对应层段的孔隙度值,建立了砂岩含量遮挡的孔隙度训练样本集构建方法.鄂尔多斯盆地东北部的致密砂岩甜点识别结果表明,孔隙度预测结果准确度高,能有效识别本区的致密储层甜点发育区.

PreviousNext No AccessSEG 2020 Workshop: Broadband and Wide-azimuth Deepwater Seismic Technology, Beijing, China, 13–15 July 2020Efficient seismic acquisition based on compressive sensingAuthors: Xiaogang HuangJianfeng ZhangDongchuan XueXiaoliu WangJicai DingZhiliang WangXiaogang HuangCNOOC Research Institute Co. Ltd.Search for more papers by this author, Jianfeng ZhangTianjin Branch, CNOOC China LimitedSearch for more papers by this author, Dongchuan XueCNOOC Research Institute Co. Ltd.Search for more papers by this author, Xiaoliu WangCNOOC Research Institute Co. Ltd.Search for more papers by this author, Jicai DingCNOOC Research Institute Co. Ltd.Search for more papers by this author, and Zhiliang WangTianjin Branch, CNOOC China LimitedSearch for more papers by this authorhttps://doi.org/10.1190/bwds2020_10.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Compressive sensing is introduced into seismic exploration to increase the efficiency of seismic acquisition. After analyzing the feasibility of compressive sensing acquisition for offshore seismic, we design the offshore seismic acquisition geometry based on the compressive sensing and prove the feasibility and validity in theory. Keywords: sampling, ocean-bottom node, ocean bottom cable, imaging Permalink: https://doi.org/10.1190/bwds2020_10.1FiguresReferencesRelatedDetails SEG 2020 Workshop: Broadband and Wide-azimuth Deepwater Seismic Technology, Beijing, China, 13–15 July 2020ISSN (online):2159-6832Copyright: 2020 Pages: 155 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 09 Nov 2020 CITATION INFORMATION Xiaogang Huang, Jianfeng Zhang, Dongchuan Xue, Xiaoliu Wang, Jicai Ding, and Zhiliang Wang, (2020), "Efficient seismic acquisition based on compressive sensing," SEG Global Meeting Abstracts : 35-37. https://doi.org/10.1190/bwds2020_10.1 Plain-Language Summary Keywordssamplingocean-bottom nodeocean bottom cableimaging PDF DownloadLoading ...

目前基于字典学习的三维地震数据重建方法通常采取二维逐切片重建的策略,这种重建方式忽略了切片间的相互联系,未能充分运用地震数据各个方向上的连续性约束.为此,提出了一种三维联合重建方法——快速结构字典学习三维数据重建方法.该方法在压缩感知理论框架下,利用快速结构字典学习算法训练训练集,产生三维自适应字典;然后利用三维自适应字典、观测矩阵以及正则化正交匹配追踪算法对数据进行高精度重建.模型数据和实际数据的重建结果表明,该方法能够恢复地震数据的细节特征,具有重建精度高、保幅性良好的优点.

PreviousNext No AccessSEG 2020 Workshop: Broadband and Wide-azimuth Deepwater Seismic Technology, Beijing, China, 13–15 July 2020Low frequency component of seismic data estimation and its application in seismic inversionAuthors: Ding JicaiZhao XiaolongJiang XiudiWang YandongHuang XiaogangWeng BinDing JicaiCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this author, Zhao XiaolongCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this author, Jiang XiudiCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this author, Wang YandongCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this author, Huang XiaogangCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this author, and Weng BinCNOOC Research Institute, National Engineering Laboratory for Offshore Oil ExplorationSearch for more papers by this authorhttps://doi.org/10.1190/bwds2020_22.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The “WBH” Technology (Wide azimuth, Broadband, and High-density) are essential quality control standards in the process of seismic data acquisition, processing, and interpretation. Among them, low frequency data plays a particularly important role in the inversion. From the perspective of acquisition, the application of low-frequency sources is an effective means to obtain low-frequency data from the source. In terms of processing, special processing techniques can also boost low-frequency components in seismic data, such as deghost process for variable depth streamer data, seismic envelope Full-waveform inversion and deep learning. During the inversion process, an effective low-frequency data can reduce the dependence of low-frequency models from logging data, it will reduce the multiplicity of inversion results, and improve the accuracy of inversion. Keywords: wide azimuth, reflection, logging, electromagnetic, full-waveform inversionPermalink: https://doi.org/10.1190/bwds2020_22.1FiguresReferencesRelatedDetails SEG 2020 Workshop: Broadband and Wide-azimuth Deepwater Seismic Technology, Beijing, China, 13–15 July 2020ISSN (online):2159-6832Copyright: 2020 Pages: 155 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 09 Nov 2020 CITATION INFORMATION Ding Jicai, Zhao Xiaolong, Jiang Xiudi, Wang Yandong, Huang Xiaogang, and Weng Bin, (2020), "Low frequency component of seismic data estimation and its application in seismic inversion," SEG Global Meeting Abstracts : 81-83. https://doi.org/10.1190/bwds2020_22.1 Plain-Language Summary Keywordswide azimuthreflectionloggingelectromagneticfull-waveform inversionPDF DownloadLoading ...

The Linxing-Shenfu block is located in the eastern margin of Ordos basin.The Tai-2tight sandstone is the main gas productive reservoir in this block.Because the reflectance signature of gas sand is sheltered by the coal seam developed above and beneath the Tai-2formation, it is difficult to identify the gas layer using the conventional seismic reservoir prediction methods.We proposed a seismic constraint-based technology for elimination of strong coal seam reflection via well-control and matching pursuit on the basis of analyzing the strong reflection amplitude characteristics of the Linxing-Shenfu block in the eastern margin of Ordos basin and identifying its genetic mechanism.This technology extracts the reasonable matched waveform of coal seam using well-side seismic traces, then dynamically adjusts the matching wavelet control parameters with the improved matching pursuit algorithm, and eliminates strong coal seam reflection from original seismic after obtaining the reflectance signature to highlight the reflectance signature of tight sandstone.The feasibility of this method is verified through numerical modelling, synthetic seismogram and the actual seismic profile, the target sand body is highlighted through eliminating the sheltering of coal seam, and the prediction results are in good agreement with the thickness of drilled sandstone.