Interpretation Of Seismic Data Research Articles

The solution of the task of dynamic interpretation of seismic data using machine learning methods

This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.

Enhancing Learning to Solve Multicomponent Fractional Viscoelastic Equations with U-net Fourier Neural Operators

Abstract The research of viscoelastic media is currently a hot topic in the interpretation and processing of seismic data. To accurately simulate the propagation of seismic waves in viscoelastic media, the fractional viscoelastic equation has emerged as an indispensable method. However, solving this equation numerically has proven to be challenging due to the complexity introduced by its fractional Laplacian operators. Recently, deep learning, especially Fourier neural operators (FNO), has shown excellent performance in learning to fast solve partial differential equations. Traditional FNO methods may face crosstalk problem and make it difficult to achieve satisfactory accuracy, when solving the multi-component fractional order viscoelastic equation. To solve this problem, we introduce a novel approach based on U-net Fourier neural operator (U-FNO). As an enhanced learning method to the traditional FNO-based method, the U-FNO-based method integrates a U-Fourier layer following the standard Fourier layer as a form of regularization, thereby achieving superior prediction accuracy for multi-component equations. Specifically, both the Fourier layers and U-Fourier layers in U-FNO are trained with the solutions of the equation from previous time steps as inputs. This training process enables the U-FNO to efficiently produce more accurate solutions for subsequent wavefield. Numerical simulations reveal that the U-FNO-based method efficiently learns to solve the fractional viscoelastic wave equation independent of fractional Laplacian operators. Additionally, U-FNO-based method offers superior prediction accuracy in comparison with the traditional FNO-based method.

Pre-stack seismic inversion based on one-dimensional GRU combined with two-dimensional improved ASPP

Abstract Pre-stack seismic inversion is essential to detailed stratigraphic interpretation of seismic data. Recently, various deep learning methods have been introduced into pre-stack inversion, effectively capturing the vertical correlations of seismic data. However, existing deep learning methods face challenges such as insufficient feature extraction, poor lateral continuity, and unclear inversion details. We introduce the atrous spatial pyramid pooling (ASPP) module into the pre-stack inversion process, modifying the connection order and mode of its three components. Additionally, we incorporate a triplet attention module to extract features at different scales and utilize a gate recurrent unit (GRU) module to extract global information. During the network training stage, we employ a multi-gather simultaneous inversion method, combining one-dimensional and two-dimensional inversions. The proposed method is named as IGIT (I for Improved ASPP, G for GRU, I for Initial model and T for Triplet attention). To verify the feasibility of this network model, we evaluate it using the Marmousi2 model, SEAM model, and field data, comparing the results with other deep learning methods. Experimental results demonstrate that the IGIT not only improves lateral continuity but also delivers accurate and clear inversion details. Notably, the inversion effect for density parameters shows significant enhancement.

Removing random noise and improving the resolution of seismic data using deep‐learning transformers

AbstractPost‐stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis‐SUnet, which achieves simultaneous random noise suppression and super‐resolution reconstruction of seismic data. First, the Conv‐Swin‐Block is designed to utilize ordinary convolution and Swin transformer to capture the long‐distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi‐scale structural similarity loss. The edge loss function directs the network training to focus more on the high‐frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis‐SUnet can effectively improve the signal‐to‐noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis‐SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.

Fault Characteristics and Reservoir Potential of Mesozoic Basins in the Southern East China Sea

The East China Sea Basin (ECSB) is an integral part of the Western Pacific tectonic system. Its development is linked to the Kula–Pacific Plate and the formation and expansion of the Philippine Sea Basin. Recent advancements in exploration technologies and theory have been applied to Mesozoic basins in the East China Sea. Researchers have posited that the southern basin has great oil and gas exploration potential. However, the characteristics and evolution of fault structures and their influence on hydrocarbon accumulation remain unclear. Here, in-depth geometric and kinematic analyses of Mesozoic fault structures in the southern ECSB were conducted using the latest interpretations of 2D seismic data and structural analysis theory. The findings revealed that the fault system was well developed and predominantly exhibited multiphase extensional and extensional-torsional features. Based on their lateral distribution and morphology, faults were categorized into three structural styles and seven combinations. According to their developmental timing, periods of active faulting were attributed to the Yanshan and Himalayan epochs. Multiphase fault activities strongly controlled the formation of traps and thus hydrocarbon accumulation, while earlier NE-trending faults controlled the formation of structural belts and hydrocarbon source areas.

Seismic reflection data interpretation and petrophysical evaluation of the Meyal area, the Potwar Basin, Pakistan

Despite extensive exploration efforts in the Potwar Basin, a thorough understanding of the structural framework and reservoir characteristics within the Meyal Field remains critical for pinpointing potential hydrocarbon zones. This study aims to integrate geology and geophysics to emphasize the structural and stratigraphic interpretation of the Meyal Field, including reservoir characterization using seismic and well-log data. The seismic interpretation of six seismic lines focuses on four key reflectors: the Kamlial, Chorgali, Sakesar, and Salt Range Formations. The Eocene Chorgali and Sakesar Formations are of primary importance for exploration and production. Structural analysis revealed that the Meyal anticline is a plunging structure bounded by a back thrust to the north and a fore thrust to the south, suggesting a favorable location for hydrocarbon accumulation. Time-depth contour maps derived from seismic data further delineate potential sites for future investigations. The well correlation indicated an uplift toward the Meyal-2 compared with Meyal-5, signifying a shallowing trend attributed to thrust tectonics. This tectonic regime has rendered the area highly prospective for hydrocarbon exploration, with thrusting and oblique-slip faulting enhancing the reservoir qualities of Eocene Formations. The petrophysical evaluation revealed favorable reservoir characteristics, including an average porosity ranging from 0% to 12% with an effective porosity of approximately 7.5%, water saturation up to 42%, and hydrocarbon saturation reaching 58% within the pay zone of the reservoir. These findings suggest that the Sakesar and Chorgali Formations within the Meyal oil field hold promise as productive hydrocarbon reservoirs. The integrated study provides valuable insights into the structural framework and reservoir characterization, highlighting the potential of the Eocene Formations as productive hydrocarbon reservoirs supported by favorable structural configurations and petrophysical properties.

Structural and Reservoir Characteristics of Potential Carbon Dioxide Storage Sites in the Northern South Yellow Sea Basin, Offshore Eastern China

The geological storage of carbon dioxide (CO2) in offshore saline aquifers stands as a primary option for reducing CO2 emissions in coastal regions. China’s coastal regions, particularly Shandong and Jiangsu provinces, face significant challenges in CO2 reduction. Therefore, evaluating the feasibility of CO2 geological storage in the adjacent seas is critical. To assess the suitability of a CO2 storage site, understanding its structural and reservoir characteristics is essential to mitigate injection and storage risks. In this study, we analyzed the structural characteristics and potential traps of the Yantai Depression in the South Yellow Sea Basin based on seismic data interpretation. We further conducted well logging analysis and post-stack seismic inversion to obtain lithological data, including acoustic impedance and sandstone content percentages from the Cenozoic Funing Formation, Dainan–Sanduo Formation, and Yancheng Formation. Our findings highlight that the Yantai Depression in the South Yellow Sea Basin exhibits diverse structural traps and favorable reservoir–caprock combinations, suggesting promising geological conditions for CO2 storage. This area emerges as a suitable candidate for implementing CO2 geological storage initiatives.

Teaching Reform of International Course on Seismic Data Interpretation in Universities under the Background of Big Data

Teaching Reform of International Course on Seismic Data Interpretation in Universities under the Background of Big Data

Seismic stratigraphy and petroleum prospectivity in the Northern Rovuma Basin, offshore Tanzania

The upper slope area of the Northern Rovuma Basin has been poorly studied and little is known about its development and petroleum prospectivity. Interpretation of wellbore, 2D and 3D seismic reflection data in this area has allowed identification of seismic features reflecting key factors for understanding the development of the Cretaceous-Holocene stratigraphy and distribution of petroleum system elements. Our results show that infill of sediment into the basin was due to an interplay of three major factors. These are the sea level variations, extensional tectonics and sediment gravity flows. The Miocene and Pleistocene-Holocene tectonic events triggered gravity flows that supplied sediments to the basin. The resultant deposits include slides, slumps, debrites and turbidites some of which contain potential reservoirs that are interpreted to have been charged by the Permo-Triassic Karoo shales and Cretaceous-Cenozoic source rocks. These reservoirs are contained in both stratigraphic and structural traps with localized combinations in some places, and are encased by deep marine shales. Some of the potential reservoirs are positively inverted and compartmentalized, and they contain several bright and flat spots suggesting hydrocarbon accumulations within the Miocene and Oligocene intervals. It has also been revealed that major gravity flows and bottom currents, that supplied and distributed sediment to the basin, were confined in three long-lived channelized systems that were initiated during the Cretaceous (due to rapid regression), and the Oligocene and Pleistocene periods (due to rapid uplift). The channels migrated toward the southeast, with some shift toward the northeast, and had overall northeast traverse direction perpendicular to the shoreline. The reported gravity flows eroded previously deposited sediment in most places causing several unconformities in the whole Cretaceous-Holocene stratigraphy. Seismic stratigraphic interpretation and facies analysis have proved useful in the identification of key petroleum system elements and improve understanding of sedimentary fill evolution of the study area.

HIERARCHICAL NEURAL NETWORKS IN PREDICTING THE PROPERTIES OF OIL AND GAS RESERVOIRS BASED ON WELL AND SEISMIC DATA

This paper describes a technique for hierarchical neural networks based on the nearest neighbor method with preliminary clustering of the original training dataset and construction of a search cluster decision tree. This method is a promising alternative to neural network technologies with deep learning and has quite a few advantages: high learning rate, identification of objects with a low degree of similarity, and the ability to generalize and retrain. As shown by testing the hierarchical neural network method on real data from the West Siberian oil and gas province, predicting the oil saturation in the Vikulov suite interval is much faster and more efficient than inversion approaches to quantitative interpretation of seismic data while achieving fairly similar geological results. This characterizes the proposed method of hierarchical neural networks as an effective tool for the quantitative interpretation of seismic data to solve geological problems.

Geophysical modeling of the middle Bogotá River Basin with support from interpretation of electrical resistivity, gravity, seismic and borehole data to evaluate potential deep aquifers

The main objective of this research was to develop a geological and geophysical modeling to infer the geometry and thickness in the sedimentary sequence of the middle basin of the Bogotá River with emphasis on the Guadalupe Group, reconstructing the stratigraphic sequence, structural setting, hydrogeological modeling, and potential geothermal uses. Various geophysical methods were applied, including vertical electrical soundings (VES), and magneto telluric soundings (MT-VES), whose results were complemented with the interpretation of seismic lines, regional and local gravity anomaly maps, and borehole data, among others, which allowed to model the subsurface from the surface to depths beyond 3000 meters, with emphasis on the interval between 500m to 1000m depth. Integrated models were developed from interpretations of electrical resistivity data, gravity and magnetic data, reflection seismic and borehole data. Based on the results, potential deep aquifers have been proposed to be confirmed by drilling. These deep aquifers can contribute to satisfy the need for water, which historically has been explored and overexploited from shallower aquifers in this sector of the basin. A recommendation was also made to consider the potential of low enthalpy thermal energy for agro-industrial purposes associated with groundwater’s temperature at the depth of this research. As a result, the modeled sedimentary sequence is characterized by a thick quaternary overburden overlying an intensively folded and faulted Neogene, Paleogene, and upper Cretaceous formations, mainly composed of siltstone, sandstones, and shale sequences of fluvial and marine environment, including facies of marine regressions and transgressions near the coastline. The penetration obtained allows establishing a high hydrogeological potential in the first 2000m depth, especially associated with the Guadalupe group, where the Labor and Tierna and Arenisca Dura formations have the highest hydrogeological potential. In addition, the preliminary estimation of thermal gradients suggest that low enthalpy geothermal energy potential is feasible to be used for the agro-industrial demand of energy of the study area.

The linear canonical W-transform

The time-frequency spectrum of seismic data is commonly used as an attribute in the analysis and interpretation of nonstationary seismic signals. To improve the capability of the W-transform to analyze nonstationary data, we introduce the linear canonical W-transform (LCWT) by implementing an affine matrix along with scaling parameters to generate high resolution and energy-concentrated time-frequency spectra. More features of nonstationary signals can be explored in the time-linear canonical frequency domain by transforming the time-frequency plane, which makes the algorithm more applicable to seismic signals. For the completeness of the LCWT, we also develop the inverse LCWT to extend its applications. Examples of synthetic and field seismic data show that a highly resolved and energy-concentrated time-frequency spectrum can be estimated with the LCWT with negligible additional computational burden. In addition, our algorithm is promising for several seismic data processing and interpretation techniques, such as reservoir characterization and thin layer identification.

Prediction of Structural Fracture Distribution and Analysis of Controlling Factors in a Passive Continental Margin Basin—An Example of a Clastic Reservoir in Basin A, South America

Structural fracture distribution is essential in oil and gas transportation and development in passive continental margin basins. In this paper, taking as an example the clastic reservoirs in the A-Basin, a passive continental margin in northeastern South America, the paleotectonic stress field of the Late Cretaceous Maastrichtian formation in Basin A was numerically simulated by finite element technique through the integrated interpretation of seismic total data, logging data and core data, and the distribution of tectonic fractures was later predicted based on rock fracture criterion. The results of the study show that: (1) The distribution of tectonic stress and fractures during the Late Cretaceous Maastrichtian formation of Basin A is affected by the fracture zone, mechanical properties of rocks and tectonic stress, regions with extensive fracture development are susceptible to stress concentrations, resulting in significant stress gradients. (2) The development of structural fractures in the study area was predicted using the Griffiths criterion, and the tensile rupture coefficient T was introduced to quantitatively characterise the intensity of fracture development, with larger values reflecting a higher degree of fracture development. The well-developed and relatively well-developed fractures are mainly located in the fracture zones and the interior of submarine fans. (3) Fracture zones and sedimentary phases mainly control structural fractures in Basin A; within 5 km outside the fracture zones, the development of fractures is controlled by the fracture zones, beyond which the regional tectonic stress field controls them; inside the sedimentary fan, the development of fractures is controlled by the sedimentary subphase, which decreases in the order of the upper fan, the middle fan, and the lower fan; inside the subphase, they are controlled by the regional tectonic stress field, and the fractures show the increasing trend in the direction of NW-NE.

The Mesozoic Subduction Zone over the Dongsha Waters of the South China Sea and Its Significance in Gas Hydrate Accumulation

The Mesozoic subduction zone over the Dongsha Waters (DSWs) of the South China Sea (SCS) is a part of the westward subduction of the ancient Pacific plate. Based on the comprehensive interpretation of deep reflection seismic profile data and polar magnetic anomaly data, and the zircon dating results of igneous rocks drilled from well LF35-1-1, the Mesozoic subduction zone in the northeast SCS is accurately identified, and a Mesozoic subduction model is proposed. The accretion wedges, trenches, and igneous rock zones together form the Mesozoic subduction zone. The evolution of the Mesozoic subduction zone can be divided into two stages: continental subduction during the Late Jurassic and continental collision during the late Cretaceous. The Mesozoic subduction zone controlled the structural pattern and evolution of the Chaoshan depression (CSD) during the Mesozoic and Neogene eras. The gas source of the hydrate comes from thermogenic gas, which is accompanied by mud diapir activity and migrates along the fault. The gas accumulates to form gas hydrates at the bottom of the stable domain; BSR can be seen above the mud diapir structure; that is, hydrate deposits are formed under the influence of mud diapir structures, belonging to a typical leakage type genesis model.

Delineation of an inversion structure of Upper Jurassic-Lower Cretaceous succession based on interpretation of seismic data of Alamein oil field, Northern Western Desert, Egypt

Delineation of an inversion structure of Upper Jurassic-Lower Cretaceous succession based on interpretation of seismic data of Alamein oil field, Northern Western Desert, Egypt

The Volcanic Rocks and Hydrocarbon Accumulation in the Offshore Indus Basin, Pakistan

To analyze the impact of volcanic rocks in the Offshore Indus Basin on hydrocarbon reservoir formation, seismic data interpretation, seismic data inversion, and sea–land correlation analysis were carried out. The results show that, longitudinally, volcanic rocks are mainly distributed at the top of the Cretaceous system or at the bottom of the Paleocene, and carbonate rock platforms or reefs of the Paleocene–Eocene are usually developed on them. On the plane, volcanic rocks are mainly distributed on the Saurashtra High in the southeastern part of the basin. In terms of thickness, the volcanic rocks revealed by drilling in Karachi nearshore are about 70 m thick. We conducted sparse spike inversion for acoustic impedance in the volcanic rock area. The results show that the thickness of the Deccan volcanic rocks in the study area is between 250 and 750 m which is thinning from southeast to northwest. Based on sea–land comparison and comprehensive research, the distribution of volcanic rocks in the Indian Fan Offshore Basin played a constructive role in the Mesozoic oil and gas accumulation in the Indus offshore. Therefore, in the Indian Fan Offshore Basin, attention should be paid to finding Mesozoic self-generated and self-stored hydrocarbon reservoirs and Cenozoic lower-generated and upper-stored hydrocarbon reservoirs.

Recovering 3D salt dome by gravity data inversion using ResU-Net++

In geophysical research, gravity-based inversion is essential for identifying geologic anomalies, mapping rock structures, and extracting resources such as oil and minerals. However, traditional gravity inversion methods face challenges, such as the volumetric effects of gravity fields and the management of large complex matrices. Unsupervised learning techniques often struggle with overfitting and interpreting gravity data. This study explores the application of various U-Net-based network architectures in gravity inversion, each offering distinct challenges and advantages. Nested U-Net, although effective, requires a high parameter count, leading to extended training periods. The recurrent residual U-Net’s implicit attention mechanism restricts its dynamic adaptability, whereas the attention U-Net’s lack of residual connections raises concerns about gradient issues. This research comprehensively analyzes the training processes, core functionalities, and module distribution of these networks, including the residual U-Net++. Our synthetic studies compare these networks with traditional focused regularized gravity inversion for reconstructing density anomalies. The results demonstrate that the nested U-Net closely approximates the actual model despite some redundancy. The recurrent residual U-Net indicates an improved alignment with minimal redundancies, and the attention U-Net is effective in density prediction but encounters difficulties in areas of low density. Notably, the residual U-Net++ excels in inversion modeling, achieving the lowest misfit percentage and accurately replicating density values. In practical applications, the residual U-Net++ impressively reconstructs the F2 salt diapir in the Nordkapp Basin with well-defined boundaries that closely match seismic data interpretations. These results underscore the capabilities of the residual U-Net++ in geophysical data analysis, structural reconstruction, and inversion, demonstrating its effectiveness in simulated settings and real-world scenarios.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology, summarizes the research status of large model technology both domestically and internationally, provides an overview of the application status of large models in vertical industries, outlines the challenges and issues confronted in applying large models in the oil and gas sector, and offers prospects for the application of large models in the oil and gas industry. The existing large models can be briefly divided into three categories: large language models, visual large models, and multimodal large models. The application of large models in the oil and gas industry is still in its infancy. Based on open-source large language models, some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation. Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models. A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation, as well as core analysis. The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models, high research and development costs, and poor algorithm autonomy and control. The application of large models should be guided by the needs of oil and gas business, taking the application of large models as an opportunity to improve data lifecycle management, enhance data governance capabilities, promote the construction of computing power, strengthen the construction of “artificial intelligence + energy” composite teams, and boost the autonomy and control of large model technology.

Understanding 3D seismic data visualization with C++, OpenGL and GLSL

Seismic data visualization in 3D space is a valuable interpretation tool. Several open-source visualization tools are available. However, little explanation is provided about the inner working of visualization process. The current work discusses a “hello world” equivalent source code for 3D seismic data visualization using Graphical Processing Units (GPUs) with OpenGL and the OpenGL Shading Language (GLSL) programming languages. Rendering is the core process generating 2D image that we see on the screen from the 3D data structures being visualized. Texture mapping-based rendering commonly applied to seismic data starts with creating an OpenGL object called texture. The texture is then mapped over a rectangular object to display a seismic line. The work presented here is performed using OpenGL, GLSL, C++ and Qt toolkit. Here Qt provides the application GUI framework, C++ is used for data I/O, filtering, and sorting, and OpenGL and GLSL are used for 3D rendering. This paper describes the data flow through the application, and two implementations of vertex and fragment GLSL shaders. Visualization is critical for seismic data processing and interpretation. The low-level details of the process presented here will hopefully help readers in obtaining better understanding of the visualization concept and inner working principle and facilitate further development.

Integration of seismic modeling and interpretation of real seismic data for the detection and characterization of carbonate stringers: example from Oman

Carbonate stringers are well known geological targets in Oman due to their high estimated prospective resources and production rate, especially in the South Oman Salt Basin (SOSB). A carbonate stringer is defined as a slab of rock completely encased inside the salt body. In this study, we integrate seismic attribute analysis, structural interpretation, seismic modeling, inversion and analog fields to understand and delineate carbonate stringers from South Oman Salt Basin. We use a newly processed seismic data acquired over the area on which we carry out a thorough seismic attribute analysis to help us build a seismic model for the salt structures of the area. A major challenge we faced in the study is the lack of well data. We thus generate pseudo wells at some selected areas based on results from our attribute analysis, structural interpretation, and initial inversion results. The pseudo wells helped improve the seismic inversion and produce more accurate impedance models. The results from seismic inversion and from seismic attribute analysis inferred that some stringers that were difficult to see in seismic data are visible in impedance and some attributes. The conducted study not only increased the confidence related to identifying and characterizing carbonate stringers in the area, but also allowed us to propose some potential stringers for future drilling programs.