The Middle-Late Triassic Yanchang Formation is the main oil-bearing unit of the Ordos Basin, China. Based on integrated field outcrop observations, laboratory analyses and seismic data, this study reconstructs the palaeoclimatic evolution and sedimentary characteristics of the Yanchang Formation. Results indicate a tripartite partitioning. Arid conditions dominated during the Ladinian Stage, followed by an abrupt shift to warm and humid conditions during the Carnian Stage, likely associated with the Carnian Pluvial Episode. Subsequently, semi-arid to subhumid and highly fluctuating regimes characterised the Norian-Rhaetian stages. Contrasting climatic conditions exerted a strong control on sediment transport processes and depositional products. During arid phases, seasonal torrential floods dominated the formation of large-scale sandbodies in the basin centre, comprising ephemeral fluvial systems and sheet flows. Conversely, during the humid CPE, intensified rainfall enhanced clastic delivery via hyperpycnal flows, turbidity currents and sandy debris flows, generating sand-rich fluvial delta and gravity flow deposits. Concurrently, the lake basin underwent rapid transgression episodes followed by oscillatory progressive regression episodes, jointly enhancing organic productivity and preservation, facilitating organic-rich source rock accumulation. Two distinct sedimentary models were proposed for the accumulation of the Yanchang Formation. During arid periods, limited catchment areas developed fluvial fan systems dominated by aggradational floodplains, seasonal channels and sheet flows, lacking progradational architecture. In contrast, during humid periods, expanded catchments hosted a complete fluvial-delta-lacustrine-gravity-flow system, where stable perennial discharge maintained relatively fixed channel pathways, with sedimentation dominated by progradation and minor retrogradation, while aggradation was negligible. An intraformational Xenoconformity, triggered by an abrupt climatic shift, exerted a first-order control on the configuration of lithologic-stratigraphic hydrocarbon traps. A combination of arid-phase low-stand fluvial fan sandstones and humid-phase transgressive organic-rich mudstones established a pervasive basin-wide sand-rich, depression-wide oil-bearing configuration. This study advances understanding of lacustrine basin evolution, sequence stratigraphy and hydrocarbon accumulation mechanisms in the Yanchang Formation while providing critical insights into continental sedimentary archives of Late Triassic global climatic extremes.

Fault surface extraction is a crucial step in fault interpretation, which aids in the analysis of subsurface oil and gas migration and reservoir distribution. One challenge of fault surface extraction is the ability to deal with complex fault situations. To enhance such ability, we transfer the fault attribute data into a multirelationship graph and employ the provided graph clustering method to obtain the distribution of each entire fault by considering the constraints of multiple interrelationships between faults. First, we develop a method for transforming fault attribute data into graph data, in which we can identify and modify problematic fault nodes, determine the edge relationships, and compute probability values that indicate the likelihood of fault nodes belonging to the same fault. Second, we propose a graph clustering method for multirelationship graphs, which can provide a systematic analysis of fault distribution by considering multiple relationships between fault nodes and obtaining the distribution of each entire fault. Finally, based on the spatial distribution of each fault, we extract all the fault surfaces in the data. The consideration of multiple relationships provides our method with the ability to figure out the distribution of faults in some complex situations, such as X-shaped faults and Y-shaped faults with similar fault orientations while avoiding producing erroneous results. We apply our method to several data and the results illustrate the effectiveness of our method. We also compare our method with an existing method, and our method outperforms the compared method by both qualitative and quantitative evaluations.

Seismic attributes are critical in understanding geologic factors, such as sand body configuration, lithology, and porosity. However, existing attributes typically reflect the combined response of multiple geologic factors. The interplay between these factors can obscure the features of the target factor, posing a challenge to its direct seismic characterization, particularly when the factor is subtle. To address this, we develop an innovative neural network designed to disentangle and characterize the individual geologic factors within seismic data. Our approach divides the geologic information in the seismic data into two categories: a single geologic factor of interest and an aggregate of all other information. A novel feature-swapping mechanism within our network facilitates the disentanglement of these two categories, providing an interpretable representation. We use a triplet loss function to differentiate data samples with similar waveforms but varying subtle geologic details, thus enhancing the extraction of distinct features. In addition, our network uses a cotraining strategy to integrate the synthetic and actual field data during the training process. This strategy helps mitigate the potential performance degradation arising from the discrepancies between simulated and actual field data. We apply our method to synthetic data experiments and field data from two geologically distinct areas. Current results indicate that our method surpasses traditional approaches, such as a deep autoencoder and a convolutional neural network classifier, in extracting seismic attributes with more explicit geophysical implications.

Well logging data contain abundant information on stratigraphic sedimentology. Artificial identification is usually strongly subjective and time-consuming. Pattern recognition algorithms like SVM may not adequately capture the depth-related variations in logging curve shape. This paper defines logging sedimentary microfacies as unidirectional 2D image segmentation and builds an improved U-net model to meet the requirements of logging sedimentary microfacies acquaintance. The proposed model contains three characteristics: (1) It removes pooling layers to avoid the loss of spatial features; (2) it utilizes multi-scale convolution blocks for mining multi-scale spatial features in logging data; (3) a one dimensional convolution layer is added to achieve deep single-direction segmentation. In this model, a 2D image composed of several standardized logging curves is used as the network's input. In addition, we propose an effective data enhancement method and calculate the geometric feature attributes of well logging curves to reduce the complexity of the data characteristics. We tested the model on manually annotated validation datasets. Our method automatically measures fine sedimentary microfacies characteristics, improving the accuracy of sedimentary microfacies identification and achieving the desired result. Additionally, the model was tested on unlabeled actual logging data, which shows the generalizability of this deep learning method on different datasets.

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults.

The Carnian Pluvial Episode（CPE） in the Triassic was one of the most abrupt known climatic events in the Earth’s history. It strongly influenced sediment deposition in inland lakes. The case considered here（the Yanchang Formation in the Ordos Basin） is the most important terrestrial petroliferous basin in China. A comprehensive review of research progress in China and elsewhere was carried out for global paleoclimate, event sedimentation, source rock development mechanisms and xenoconformity in terrestrial basins. Rethinking and investigating some of the sedimentation and accumulation questions related to the Yanchang Formation has revealed the following three points.（1） The Yanchang Formation, which developed during the Carnian stage at the beginning of the Upper Triassic, is redefined and its sedimentary depositional response to the CPE is clarified. The upper boundary is equivalent to the stratigraphic boundary between the Chang 4 + 5 members and the Chang 3 member. The CPE roughly corresponds to the deposition of the so-called “Chang 7 member black shale event”.（2） During the Carnian and CPE, the Yanchang Formation sedimentary lake basin underwent rapid transgression and oscillating slow retreat, very conducive to the development of high-quality hydrocarbon source rocks.（3） Due to the paleoclimatic conditions and CPE, upper and lower xenoconformities were formed in response to the abrupt climatic and environmental changes. These both significantly influenced the distribution of oil and gas. This study is of positive significance for in-depth discussion of the formation and evolution mechanisms of the Yanchang Formation lake basin, as well as sedimentary sequence filling and evolution, and as a guide for oil and gas exploration.

The establishment of a three-dimensional velocity field is an essential step in seismic exploration, playing a crucial role in understanding complex underground geological structures. Accurate 3D velocity fields are significant for seismic imaging, observation system design, precise positioning of underground geological targets, structural interpretation, and reservoir prediction. Therefore, obtaining an accurate 3D velocity field is a focus and challenge in this field of study. To achieve intelligent interpolation of the 3D velocity field more accurately, we have built a network model based on the attention mechanism, JointA 3DUnet. Based on the traditional U-Net, we have added triple attention blocks and channel attention blocks to enhance dimension information interaction, while adapting to the different changes of geoscience data in horizontal and vertical directions. Moreover, the network also incorporates dilated convolution to enlarge the receptive field. During the training process, we introduced transfer learning to further enhance the network's performance for interpolation tasks. At the same time, our method is a deep learning interpolation algorithm based on an unsupervised model. It does not require a training set and learns information solely from the input data, automatically interpolating the missing velocity data at the missing positions. We tested our method on both synthetic and real data. The results show that, compared with traditional intelligent interpolation methods, our approach can effectively interpolate the three-dimensional velocity field. The SNR increased to 36.22 dB, and the pointwise relative error decreased to 0.89%.

Interference of thin-interbedded layers in seismic reflections has great negative impact on thin-interbedded reservoirs prediction. To deal with this, two novel methods are proposed that can predict the thin-interbedded reservoirs distribution through strata slices by suppressing the interference of adjacent layer with the help of seismic sedimentology. The plane distribution of single sand bodies in thin-interbedded reservoirs can be clarified. (1) The minimum interference frequency slicing method, uses the amplitude-frequency attribute estimated by wavelet transform to find a constant seismic frequency with the minimum influence on the stratal slice of target layer, and then an optimal slice corresponding the constant frequency mentioned above can be obtained. (2) The superimposed slicing method can calculate multiple interference coefficients of reservoir and adjacent layers of target geological body, and obtain superimposed slice by weighted stacking the multiple stratal slices of neighboring layers and target layer. The two proposed methods were used to predict the distribution of the target oil layers of 6 m thick in three sets of thin-interbedded reservoirs of Triassic Kelamayi Formation in the Fengnan area of Junggar Basin, Northwestern China. A comparison with drilling data and conventional stratal slices shows that the two methods can predict the distribution of single sand bodies in thin-interbedded reservoirs more accurately.

GeoSed是针对薄互储层沉积和储层分布预测研发的软件系统,软件遵循地震沉积学研究规范设计,包括工区与数据管理、合成记录、测井相分析、地震沉积分析和地质成图5个子系统29个功能模块,具备等时格架建立、沉积特征加强、薄层沉积分析、沉积体系分析和配套功能五大技术系列,形成了"逐级标定、井震匹配、动态分析、目标评价"一体化方案,可大幅提高工作效率及沉积储层分析精度,对提高岩性地层油气藏勘探开发成功率意义重大,是支撑我国油气勘探开发的强有力工具.

In this paper, we propose a novel approach of deep-learning-based seismic horizon auto-picking that introduces a modified vector quantized variational autoencoder (VQ-VAE) framework to improve the accuracy of seismic horizon interpretation and, for the first time, quantitatively evaluate the uncertainty of the auto-picked horizon by exploiting the concept of entropy. Compared with the conventional VQ-VAE approach, the proposed method not only modifies the VQ-VAE model with more deep-learning channels at each layer of the network to enhance the performance of horizon auto-picking within the VQ-VAE framework, but also extends the 1D seismic labels with more continuous samplings within a single trace to boost the stability of auto-picked horizon in geologically complex settings and also significantly suppress the resulting uncertainty. To further improve the resulting accuracy in geologically complex settings, we introduce the directional structure tensor to extract a more reliable initial horizon and, moreover, a dilated horizon searching strategy to extend the capacity of the proposed method in dealing with the large fault displacement and reducing the computational cost simultaneously. Additionally, the resulting uncertainty quantitatively measured by entropy can also serve as an effective indicator to enable a further refinement of the auto-picked result accordingly. Both 2D example and 3D field applications are carried out to validate the effectiveness of the proposed method.

Underground flow paths (UFP) often play an important role in the illustration of geological data by geologists, especially in illustrating geological data and revealing stratigraphic structures, which can help domain experts in their exploration of petroleum information. In this paper, we present a new immersive visualization tool to help domain experts better illustrate stratigraphic data. We use a visualization method based on bit-array-based 3-D texture to represent stratigraphic data. Our visualization tool has three major advantages: it allows for flexible interaction at the immersive device, it enables domain experts to obtain their desired UFP structure through the execution of quadratic surface queries, and supports different stratigraphic display modes, as well as switching and integration geological information flexibly. Feedback from domain experts has shown that our tool can contribute more for domain experts in the scientific exploration of stratigraphic data, compared to the existing UFP visualization tools in the field. Thus, experts in geology can have a more comprehensive understanding and more effective illustration of the structure and distribution of UFPs.

The traditional constant time window-based waveform classification method is a robust tool for seismic facies analysis. However, when the interval thickness is seismically variable, the fixed time window is not able to contain the complete geologic information of interest. Therefore, the constant time window-based waveform classification method is inapplicable to conduct seismic facies analysis. To expand the application scope of seismic waveform classification in the strata with varying thickness, we have proposed a novel scheme for unsupervised seismic facies analysis of variable window length. The input of the top and bottom horizons can guarantee the comprehensive geologic information of the target interval. Throughout the whole workflow, we use the dynamic time warping (DTW) distance to measure the similarities between seismic waveforms of different lengths. First, we improve the traditional spectral clustering algorithm by replacing the Euclidean distance with the DTW distance. Therefore, it can be applicable in the interval of variable thickness. Second, to solve the problem of large computation when applying the improved spectral clustering approach, we adopt the method of seismic data thinning based on the technology of the superpixel. We combine these two algorithms and perform the integrated workflow of improved spectral clustering. The experiments on synthetic data show that the proposed workflow outperforms the traditional fixed time window-based clustering algorithm in recognizing the boundaries of different lithologies and lithologic associations with varying thickness. The practical application shows great promise for reservoir characterization of interval with varying thickness. The plane map of waveform classification provides convincing reference to delineate reservoir distribution of the data set.

薄储层是近年来我国油气勘探的重点目标之一,提高地下薄储层识别能力对油气勘探具有重要意义.对于薄储层预测,由于地震分辨率及邻层干涉等因素的制约,基于反射地震数据直接探测地下薄储层的难度较大.为此,采用一种新的稀疏贝叶斯学习理论开展地震反射系数反演,并在获取地层反射系数的基础上计算地层相对波阻抗信息,通过设计线性FIR滤波器滤除地层相对波阻抗计算过程中的低频累积误差,进而开展薄储层高精度预测.实际资料应用表明:新的基于稀疏贝叶斯学习理论的地震反射系数反演方法可大幅度提高地层反射系数的计算精度,为获取高精度地层相对波阻抗奠定了基础;设计的线性FIR滤波器能够有效拟制地层相对波阻抗中的低频累积误差,提高了薄储层识别精度.与传统地震振幅属性相比,本次研究获取的地层相对波阻抗信息能更精确地表征薄储层平面形态展布特征,并能有效提高薄储层勘探的成功率.

In view of insufficient application of seismic data in sequence stratigraphic analysis and the inconsistency between drilling sequence division and seismic sequence division, principles and relevant corollary techniques for high-resolution seismic sequence analysis under the constraint of log sequence have been proposed. First of all, seismic data is converted into relative impedance. The relative impedance data volume is processed through seismic reflection structure fine processing and converted into Wheeler domain. Seismic data is unfolded in the frequency domain to generate the time-frequency feature chart. Both the Wheeler chart and the time-frequency feature chart can reflect changes in sedimentary cycles. Through fine well-to-seismic calibration, results of log sequence analysis can be combined with seismic data. Through calibration and verification of changes in sedimentary cycle reflected in the Wheeler chart and time-frequency feature chart, high-resolution isochronous formation framework can be established for the entire basin. Application of this method in the A Basin in Eastern China has achieved outstanding performances in exploration.

A method of identifying lithostratigraphic traps based on seismic sedimentology is proposed. We first establish a 3D high-resolution (fifth-order) sequence stratigraphic framework by using the stratal slices. Then, the reservoir distribution and reservoir-seal assemblage are investigated within the high-resolution sequence framework. This method turns the interpretation of lithostratigraphic traps from traditional seismic facies-based approach to the dynamic analysis of high-resolution seismic geomorphic information. We divide the lower Sha-1 member in the Banqiao Sag, Bohai Bay Basin, East China, into fourth- and fifth-order sequences by applying our method. The fifth-order sequence corresponding to Sha1-2 shows that the fan delta-distal subaqueous fan depositional system can be observed at the bottom of Sha1-2. The bounding fault and paleogeomorphology control the deposition of sand, whereas the sand bodies in the fan delta and distal subaqueous fan are developed near the bounding fault and the open lake basin, respectively. We then predict the sand thickness based on the well logs and seismic amplitudes. Moreover, according to the structural features, at least four lithostratigraphic traps are identified. These traps form a good reservoir-seal assemblage with overlying mudstones deposited during the period of lacustrine level rising. The drilling results in Trap-3 suggest that, our method can be a method of choice for effectively identifying the lithostratigraphic traps, a significant measure for hydrocarbon exploration.

Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization. The current seismic waveform clustering algorithms are predominantly based on a fixed time window, which is applicable for layers of stable thickness. When a layer exhibits variable thickness in the seismic response, a fixed time window cannot provide comprehensive geologic information for the target interval. Therefore, we propose a novel approach for a waveform clustering workflow based on a variable time window to enable broader applications. The dynamic time warping (DTW) distance is first introduced to effectively measure the similarities between seismic waveforms with various lengths. We develop a DTW distance-based clustering algorithm to extract centroids, and we then determine the class of all seismic traces according to the DTW distances from centroids. To greatly reduce the computational complexity in seismic data application, we propose a superpixel-based seismic data thinning approach. We further propose an integrated workflow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms. We evaluated the performance by applying the proposed workflow to synthetic seismograms and seismic survey data. Compared with the the traditional waveform clustering method, the synthetic seismogram results demonstrate the enhanced capability of the proposed workflow to detect boundaries of different lithologies or lithologic associations with variable thickness. Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workflow correlates well with the geological characteristics of wells in terms of reservoir thickness.

Summary Underground flow path (UFP) is one of the most significant stratigraphic structures in revealing the distribution of oil or gas from seismic data. We design a domain‐specific visualization system to extract the stratigraphic structures by seed point tracing and explore the seismic data by graph interactions. The seeds are automatically generated by kernel function–based density gradients computation. Users are allowed to adjust the recommended seeds by fine‐tuning them with visual interactions. The seeds are further merged by a weighted quick‐union algorithm to get the link information to construct a graph. Different types of nodes in the graph are designed to enable users to explore the extracted UFP structures intuitively. Finally, we evaluated the proposed approach by performance tests, sensitivity tests, and ground truth tests. The feedback from the domain experts demonstrates that the proposed visualization tool improved the capability of revealing the distribution and geostructures of UFPs compared with the existing methods.

In this article, the authors propose a stratigraphic slice interpretative visualization system, namely slice analyzer. It enables the domain experts, i.e., geologists and oil/gas exploration experts, to interactively interpret the slices with domain knowledge, which helps them get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP), river delta, floodplain, slump fan, etc. In addition to some domain-specific slice edit manipulations, a sketch-based sub-region partitioning approach is further presented to help users divide the slice into individual sub-regions with homologous characteristics according to their domain knowledge. Consequently, the geological materials they are interested in can be extracted automatically and visualized by the proposed geological symbol definition algorithm. Feedback from domain experts suggests that the proposed system is capable of interpreting the stratigraphic slice, compared with their currently used tools. (C) 2019 Society for Imaging Science and Technology.

Abstract. Seismic data visualization and analysis can help the domain experts, e.g., geologists and oil or gas exploration experts, to explore the distribution of petroleum or gas. It assists them to get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP) and the contexts of UFPs (river delta, floodplain, slump fan, oil well, etc.). UFPs are one of the significant stratigraphic structures according to the domain experts, because they are closely related to the distribution and the migration of oil or gas. We design a quadratic-surface distance query scheme to explore UFPs and their contexts within a local region. First, it just needs to share parameters of quadratic surfaces to the rendering modules instead of all volume data or all subvolume data to conduct distance queries, and it is flexible to perform multiple complex logic operations through the quadratic surface-based queries. Second, it enables one to perform domain-specific interactions after distance queries such as the flexible switching of multiple display modes. Third, it enables one to perform local transfer function on different subvolumes different query results or their arbitrary combinations. We have evaluated the approach by comparing them with existing methods by performance evaluation and result evaluation. Results show that the proposed approach is capable of performing complex distance queries and fulfilling the domain-specific interactions getting better results and timing performance.