Chapter 13 - Understanding Seismic Interpretation Methodology

In recent years the Arctic has come into focus for hydrocarbon exploration, and areas offshore both West and East Greenland have been evaluated as promising frontier hydrocarbon provinces. Seven hydrocarbon exploration and exploitation licenses were awarded in 2007–2008 offshore the Disko–Nuussuaq region (Fig. 1), and two more have been awarded in the open-door region offshore south-western Greenland. In 2007, an extensive amount of new seismic and aeromagnetic data was acquired by the TGS-NOPEC Geophysical Company in the north-eastern Baffin Bay region. Geophysical mapping has been initiated by the Geological Survey of Denmark and Greenland (GEUS) in the Melville Bugt region offshore North-West Greenland (Fig. 1) with the purpose of evaluating the hydrocarbon prospectivity. Initial interpretation of seismic and gravity data suggests the presence of deep sedimentary basins separated by structural highs. Geological information on source rock, reservoir rock and seal intervals from surrounding regions suggest that the Melville Bugt region is likely to have a significant petroleum potential. The study is based on public domain magnetic and gravity data, and all proprietary and public 2-D seismic data (Fig. 1) acquired before 2003. Seismic horizons from the ‘seismic basement’ to ‘base Quaternary’ are being interpreted regionally. Based on the seismic interpretation, a structural element map, depth-structure maps and isopach maps will be produced in order to assess the prospectivity of the Melville Bugt region.

Previous No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021Enhancing seismic fault extraction through multiple ant tracking on cloud environmentAuthors: Mohamed AmroucheAyako OtakaraKwangho LeeMisa OkadaMohamed AmroucheSchlumbergerSearch for more papers by this author, Ayako OtakaraSchlumbergerSearch for more papers by this author, Kwangho LeeSchlumbergerSearch for more papers by this author, and Misa OkadaSchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segj2021-091.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Manual seismic faults interpretation can be one of the most time-consuming tasks in reservoir modeling. To accelerate the fault extraction process, some existing methods based on seismic attributes analysis such as the Ant Tracking method are already well adapted by experienced interpreters. The Ant Tracking algorithm is an innovative workflow that introduces an original paradigm in fault interpretation, by emulating the behavior of ant colonies in nature to track and extract faults and fractures network from the seismic data. This method has already showed good results on improving seismic interpretation workflows. However, some of the limitations of the ant tracking remain the range of uncertainty on properly mapping the fault continuity in low quality seismic data, and deeper structures where the seismic noise is more prominent. The Ant Tracking process also requires high spec hardware to properly perform, specially for large 3D seismic volumes. In this study, we proposed a new approach for Ant Tracking interpretation by averaging multiple estimations of the seismic data using different seismic attributes and parameters tunings. Our results show that the proposed combined and iterative strategy enables to effectively reduce the noise level in the seismic data and improve the continuity of the faults network, taking advantage of the powerful cloud environment to enhance the computation speed. Keywords: seismic interpretation, ant tracking, automated fault extraction, seismic attributesPermalink: https://doi.org/10.1190/segj2021-091.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Mohamed Amrouche, Ayako Otakara, Kwangho Lee, and Misa Okada, (2021), "Enhancing seismic fault extraction through multiple ant tracking on cloud environment," SEG Global Meeting Abstracts : 346-349. https://doi.org/10.1190/segj2021-091.1 Plain-Language Summary Keywordsseismic interpretationant trackingautomated fault extractionseismic attributesPDF DownloadLoading ...

Interpretation of faults in seismic data has a significant impact on hydrocarbon exploration, reservoir characterization, and field development workflows. Faults represent drilling hazards, often control fluid migration, and define the compartmentalization of reservoirs. Thus, a comprehensive and uncertainty-aware understanding of faulting in a reservoir is crucial for efficient development of hydrocarbon resources. Fault interpretation is typically performed manually, and hence is both time and labor intensive. Fault interpretations differ significantly due to the experience of the interpreter, the interpretation objectives (structural versus stratigraphic), the data preconditioning, and visualization settings. In addition to the human influences, seismic images also suffer from various imperfections such as velocity modeling errors and noise. Consequently, fault interpretation is subject to uncertainties, both in terms of whether a fault is present and, if so, the precise location, fault-network connectivity, and orientation. These uncertainties are very consequential in determining the underlying geological model. Our novel approach proposes to make estimation of fault uncertainty an integral part of any interpretation by leveraging modern cloud computing technologies and new efficiencies that can be gained through the automation of the fault interpretation process. We compute multiple fault attributes from a plurality of input image volumes which produces a statistically valuable population of fault attribute realizations. We leverage this redundancy to derive qualitative measures of fault uncertainty such as fault probability corridors that can guide an uncertainty-aware fault interpretation. Finally, we compute geometrical properties of fault-plane patches that we use to derive quantitative measures of fault dip and fault location perturbations.

Fault interpretation is a key link in seismic data interpretation. With the continuous increase in seismic data volume in original production, manual fault picking cannot efficiently handle massive seismic data. With the development of artificial intelligence technology, the automatic and rapid picking of faults has become a hot topic in the application of deep learning methods in the field of seismic data interpretation. Therefore, this paper proposes a seismic data fault identification method based on SEU-Net. The SEU-Net network introduces SE blocks on the basis of the original U-Net network, enhancing the weights of effective channels through adaptive feature recalibration, and combines a multi-scale feature fusion strategy to improve the model’s ability to identify fault edges and minor faults. The experimental results show that compared with the original U-Net network, the SEU-Net network exhibits higher fault identification accuracy and robustness both in synthetic seismic data and original work area data. This research provides an efficient and automated solution for fault detection in seismic data and it holds certain theoretical value and practical application potential.

This research presents a comprehensive petrophysical evaluation of the Otio Field, located in the onshore Niger Delta Basin, with a focus on characterizing hydrocarbon-bearing reservoirs. A multidisciplinary approach was employed, integrating well log data and seismic interpretation to analyze key petrophysical parameters across five hydrocarbon-bearing horizons (Sands D, E1, E2, H, and J). The petrophysical analysis revealed porosity values ranging from 18% to 27%, water saturation between 20% and 31%, and Net-To-Gross (NTG) ratios of 59% to 96%. These parameters were used to evaluate the reservoir quality and hydrocarbon potential of each horizon. The structural framework of the field was also evaluated, with 16 faults mapped, including major trapping structures F6 and F7. Time and depth structure maps were generated for each horizon, enabling accurate volume estimations. The results indicate that the North-Eastern prospect is the most promising, particularly in Sand E2, which exhibits the highest hydrocarbon volume in place. This study provides valuable insights into the petrophysical properties of the Otio Field reservoirs, offering a basis for prospect ranking and guiding future exploration efforts. Keywords: Petrophysical analysis, volumetric estimation, hydrocarbon reserves, well logs, seismic interpretation.

In some oilfield where 3D seismic survey has been done, the deeper structure cannot be discovered for poor deep seismic data. Layer stripping technique applied both seismic and gravity data is a solution, but it cannot get satisfied processing effect because the horizontal variation of formation density is ignored. To improve the gravity survey effect in 3D seismic survey area, this paper presents the formation separation technique. Based on 3D seismic depth data and the transformed density data from 3D seismic velocity data, the upper formation's gravity effects are calculated and removed from Bouguer gravity and then the formation‐separated gravity‐anomaly was obtained, which mainly reflects the deeper geological structure. In block XX, the seismic data of shallow formations are excellent but that of top of basement are poor. The formation‐separated gravity‐anomaly processed under the control of 3D seismic data fits well with the known seismic interpretation and wells. It makes the geological interpretation more reliable

The development of seismic technology has made seismic data to be widely used in the interpretation of stratigraphic sequence frames, reservoir identification, fluid detection, and other research fields involved in reservoir description. The 3D technology reservoirs have always been the focus, as well as difficulty, of research. With the rapid development of information technology and the continuous improvement of seismic exploration level, people have put forward higher requirements for the accuracy of seismic data interpretation results. Aiming at the large number of structural and unstructured data in seismic, logging, geology, and other disciplines involved in seismic interpretation, how to effectively organize and coordinate analysis to discover the hidden reservoir structure and oil and gas distribution information has always been a geological and important topic for information processing technicians. This thesis is aimed at the current high-water-phase development of Shengtuo Oilfield reservoir and the problems existing in geological research. Based on seismic structural interpretation and attribute analysis, this paper analyzes the reservoir structural characteristics, sedimentary characteristics, and reservoir physical parameter characteristics based on geology, logging interpretation, core analysis, drilling, and seismic interpretation. Using the kriging method with external drift can cooperate with seismic variables to establish a reservoir geological model to study the Shengtuo Oilfield reservoir. We combine artificial intelligence technology with geological modeling technology of seismic interpretation results to explore the best method for predicting earthquakes. The research results in this paper show that the relative error of the model established by the kriging method in the article is relatively small for thinning wells, mainly concentrated around 1%. Examination of the thinning wells of 45 wells shows that the model established is basically good and the example has high accuracy. The research results in this paper have a guiding study of distribution and tapping potentials in the study area, formulating reasonable development and adjustment plans and improving oil recovery.

Seismic is one of the main methods used for the identification of structures and stratigraphic studies in sedimentary basins. In the Santos Basin, numerous 2D and 3D seismic surveys are being conducted in order to get a better ima-ge of the geological section to depths beyond the base of salt layer. Crustal modelling studies that make joint interpretation of seismic and gravity data are found in the literature, however there are few studies that relate gravity anomalies directly to salt structures. This work aims to associate gravity anomalies with salt structures from seismic and gravimetric interpretation. For studies aimed to model the crustal structure from gravity field data, the knowledge of two major discontinuities is required, the basement and the Moho. Such interfaces are often not easily seen by seismic and so, during this study, they were found by different methods involving analysis of gravity anomalies. The other interfaces involving density contrasts were analyzed based on seismic interpretation. The results showed that the obtained seismic geological interpretations may provide additional information when compared to gravity anomaly data. In all the modelled profiles, some geological information of the Santos Basin that are not visible in the seismic, could be interpreted according to the geological model and the adjustment of gravity anomaly curves. As a final conclusion of this work, it is suggested that the combined analysis of the two cited geophysical methods, can provide important information about the crustal structure and to assist in modelling the salt layer.

Seismic is one of the main methods used for the identification of structures and stratigraphic studies in sedimentary basins. In the Santos Basin, numerous 2D and 3D seismic surveys are being conducted in order to get a better ima-ge of the geological section to depths beyond the base of salt layer. Crustal modelling studies that make joint interpretation of seismic and gravity data are found in the literature, however there are few studies that relate gravity anomalies directly to salt structures. This work aims to associate gravity anomalies with salt structures from seismic and gravimetric interpretation. For studies aimed to model the crustal structure from gravity field data, the knowledge of two major discontinuities is required, the basement and the Moho. Such interfaces are often not easily seen by seismic and so, during this study, they were found by different methods involving analysis of gravity anomalies. The other interfaces involving density contrasts were analyzed based on seismic interpretation. The results showed that the obtained seismic geological interpretations may provide additional information when compared to gravity anomaly data. In all the modelled profiles, some geological information of the Santos Basin that are not visible in the seismic, could be interpreted according to the geological model and the adjustment of gravity anomaly curves. As a final conclusion of this work, it is suggested that the combined analysis of the two cited geophysical methods, can provide important information about the crustal structure and to assist in modelling the salt layer.

Thin (<20 m), sand injectites, too small to resolve in conventional seismic reflection data, can constitute a large part of the net-to-gross volume and affect fluid flow in the reservoir. However, they may also cause challenges for well placement and reservoir development because they are too small to be reliably imaged in seismic reflection data. It is therefore important to better understand how small-scale injectites influence seismic images, and how they may be recognized and characterized above reservoirs. The Grane Field (North Sea) hosts numerous small-scale sand injectites above the main reservoir unit, challengin well placement, volume estimates and seismic interpretation. Here, we investigate how such small-scale sand injectites influence seismic images and may be characterized by (1) using well, 3D seismic and outcrop data to investigate geometries of small-scale sand injectites (0–15 m thick), (2) performing seismic convolution modelling to investigate how these would be imaged in seismic data, and (3) comparing these synthetic seismic images with actual 3D seismic data from the well-investigated Grane Field. Our results show that despite injectites being below seismic resolution, small-scale sand injectites can be detected in seismic data. They are more likely to be detected if they have high thickness (>5 m), steep dip (>30°), and when densely spaced. Furthermore, as the fraction of sand injectites increases the top reservoir amplitude will decrease. Moreover, comparison of the synthetic seismic images with real seismic data from the Grane Field indicates that the low-amplitude anomalies and irregularities observed above the reservoir may be a result of the overlying sand injectites. Additionally, the comparison strongly suggests that the Grane Field hosts sand injectites that are thicker and located further away from the top reservoir than is indicated by well observations. These results may be used to improve well planning and develop reservoirs with overlying sand injectites. Supplementary material: A PDF file containing all the seismic modelling results allowing the reader to flip back and forth between the models is available at https://www.doi.org/10.6084/m9.figshare.14333102 and well logs from well 25/11-18T2 are available at https://factpages.npd.no/pbl/wellbore_documents/2358_25_1_18_COMPLETION_REPORT_AND_LOG.pdf

Hydrocarbon exploration and production depend on the classification of seismic facies, a basic component of subsurface reservoir characterization. Conventional interpretation techniques mostly rely on subjective, time-consuming, and inconsistent manual examination of seismic data. Automated methods have become effective instruments for increasing seismic interpretation efficiency and accuracy as deep learning has advanced. In this work, a deep learning framework for classifying seismic facies using a Convolutional Neural Network (CNN) based on U-Net is presented. To assess the model's prediction power, it was tested on an unlabeled dataset after being trained on labeled seismic data. To improve model performance, data preprocessing methods such as one-hot encoding, normalization, and image scaling were used. Because of the magnitude of the dataset and the computational complexity, training was done on Google Collab. Techniques for data augmentation were also used to enhance generalization. Numerous performance measures were used to assess the algorithm, proving its efficacy in classifying facies. The findings show that by automating classification, decreasing manual labor, and enhancing the dependability of subsurface analysis, deep learning considerably improves seismic interpretation. The trained model's potential for scalable deployment in oil and gas exploration was demonstrated by the fact that it was preserved for later use.

Seismic inversion methods are routinely used to estimate attributes such as P-impedance, S-impedance, density, P-wave and S-wave velocity, and elastic impedances from seismic and well log data. These attributes help to understand subsurface lithology and fluid content. There are several types of seismic inversion methods available in which model-based inversion has got more attention. In the present study, the model-based inversion and geostatistical methods namely probabilistic neural network (PNN), multi-layer feed-forward neural network (MLFN) and radial basis function neural network (RBFNN) are implemented to estimate acoustic impedance, porosity, density, gamma-ray volume and P-wave velocity in the inter-well region of the F-3 block, Netherlands. The aim of the study is to predict a number of petrophysical parameters and demonstrate how they can be used to interpret seismic reflection data. A comparative study is also performed to estimate the best suitable method to predict various petrophysical parameters. The assessment of inverted results demonstrates that the impedance in the region varies from 2550 m/s*g/cc to 6150 m/s/*g/cc, which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and initial seismic trace is estimated to be 0.98, and the synthetic relative error as 0.19, indicating excellent algorithm output. Further, the assessment of the geostatistical methods is carried out to predict petrophysical parameters in conjunction with Multi-Attribute Transform Analysis. The analysis shows that among the geostatistical methods, the PNN provides slightly better results. The petrophysical parameters that are estimated in this study are porosity, density, gamma-ray, and P-wave velocity volumes. These parameters reinforce the interpretation of seismic data, which is a significant step in any project of exploration and production. The technique is first implemented to the composite traces near to the well location, and the findings are analysed with well log data. Second, the entire seismic section is inverted for porosity, density, gamma-ray, and P-wave velocity after achieving satisfactory outcomes in the first step. The assessment demonstrates very high-resolution subsurface petrophysical parameters which strengthen the seismic data interpretation.

Seismic interpretation conveys the geologic meaning of data by extracting subsurface information from it. Type of interpretation depends on the geologic objectives linked to the phase of exploration and on the kind of available data, 2D/3D, its grid density and quality. 2D seismic data interpretation can be typed as structural, stratigraphic and seismic stratigraphy. Workflows for the 2D interpretation types are described in this chapter, underscoring the basic principles along with some of the application shortcomings that crop up during normal interpretation practices. Techniques of seismic calibration, horizon correlation, preparing seismic maps, and velocity modelling and depth conversion are elaborated with examples and illustrations. Seismic stratigraphy is typically a regional geologic interpretation of seismic 2D data, and is described with schematics and real seismic image illustrations. The difference between seismic stratigraphy and stratigraphic interpretation is highlighted.It is necessary the seismic interpreter stresses on the consequences of interpretation results that impact the geological and engineering issues in the exploration project. This is a process which adds value to interpretation and may be termed as data evaluation. Data evaluation looks ahead beyond the usual outputs of routine interpretation and assists in review and assessment of economic viability of prospects which enables management to strategize exploration policies. This is emphasized by citing examples and illustrations in the chapter.

Seismic processing is expected to deliver a reliable product for seismic structure interpretation and reservoir characterization to support subsequent reservoir modeling and drilling operation. 3D OBC wide-azimuth (WAZ) seismic data has a significant advantage in this respect, since it contains large offset and wide azimuth information, which can be utilized for amplitude versus offset (AVO) and amplitude versus azimuth (AVAz) analysis respectively. However, these advanced studies require area-specific data processing efforts in this region for strong linear noise, multiple noise, designature uncertainty etc. Seismic data re-processing was newly conducted in pre-stack time migration (PrSTM) sequence to overcome such technical challenges. Surface-wave noise attenuation was achieved by 2D and 3D model-based dispersive energy attenuation and multi-pass structure oriented filtering in pre-stack and post-stack stages. Source-side deghosting highlighted a low frequency residual bubble and was followed by another pass of de-bubbling based on a smooth spectrum assumption. Shallow water reverberation was minimized by 3D deconvolution and high-resolution radon demultiple. 5D regularization and interpolation with trace densification provided evenly populated traces for each angle and azimuth sectors. Intensive signal preservation protected diffraction energy and resulted in better fault imaging than the vintage processing outcome. Both the vintage and the re-processed seismic data were evaluated in quantitative manners to compare with each other. Angle stacks from the re-processing showed more reasonable response for AVO analysis. Azimuth stacks showed stable S/N ratio leading to higher confidence in azimuthal anisotropy analysis. All these seismic deliverables enable accurate elastic property estimation and detail fault interpreation at target reservoir intervals. The novelty of the proposed workflow is to overcome common technical problems with targeted noise and multiple reduction focusing on angle / azimuth stacks. It unlocks advanced geophysical study for carbonate reservoir in shallow water areas. Processing deliverables will be fully utilized in fault interpretation, AVO and AVAz analysis and will support the forthcoming field development activity.

Seismic interpretation is an essential factor in the success of field development projects because it allows geologists and engineers to better understand subsurface formations and identify potential resources; however, noisy seismic data, especially in regions with complex geological structures, frequently hinder this process. Such noise emerging from equipment limitations, environmental interference, and inherent geological complexities can obscure essential geological features, making accurate subsurface imaging challenging and reducing the effectiveness of traditional interpretation methods. This paper presents a machine learning (ML) method to enhance the conditioning of 2D and 3D post-stack seismic data by targeting noise and migration artifacts. These issues are often responsible for compromising the quality of seismic data sets. The method this paper presents leverages convolutional neural networks (CNNs) to identify and correct spatial inconsistencies within the data. With minimal preprocessing, requiring only the conversion of data into a 3D NumPy array format, this approach simplifies the workflow while improving data clarity and accuracy. Early statistical analysis of outliers and data normalization provides the ML model with stable inputs, allowing it to iteratively train on raw data and infer the original signal patterns in an unsupervised manner without the need for labeled data sets. The model outputs a conditioned version of the seismic data, significantly reducing noise while preserving critical geological features. The results demonstrate that ML seismic conditioning improves fault imaging, horizon continuity in areas with low signal-to-noise ratios (SNR), and localized smoothing without compromising resolution. Furthermore, the method presented in this paper retains signal fidelity and relative amplitude strengths, ensuring that geological interpretations remain accurate and reliable. By automating the traditional manual task of data conditioning, the proposed solution optimizes interpreter workflows, allowing them to focus on higher-level geological analysis and decision-making. This approach ultimately increases the accuracy of subsurface interpretations and resource exploration, improving the overall quality and efficiency of seismic interpretation projects. In addition, for 2D seismic, traditional methods of denoising rely on expert judgement using tools such as f-x deconvolution. 2D CNNs offer an efficient alternative, especially when training data can be synthesized and leveraged in supervised learning. This method can help the CNN identify and remove noise while preserving pertinent subsurface structures. This paper presents a 2D CNN architecture containing a model trained with synthetic seismic data to improve SNR of field seismic data; hence, interpretation.