86th EAGE Annual Conference & Exhibition
- Conference date: June 2-5, 2025
- Location: Toulouse, France
- Published: 02 June 2025
1 - 20 of 951 results
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Fast and Accurate Cumulant-Based Mixed-Phase Wavelet Estimation for High-Resolution Seismic Data
More LessAuthors A. Egorov, P. N. Aleksandrov, S. Vakulenko and S. BuryakWavelet estimation is a key processing step in high-resolution and ultra-high-resolution seismic processing. Stacking seafloor reflection waveforms is a commonly used technique to obtain a wavelet. While easy to implement in practice, it requires a picked seafloor. To address this limitation, automatic mixed-phase wavelet estimation techniques can be utilized. Fourth-order cumulant matching is one of such techniques. It conducts a time-domain inversion to find a wavelet with a fourth-order moment equal to the fourth-order cumulant of the data. However, this method is computationally expensive. We propose a modification of this technique to use third-order cumulants, significantly increasing the algorithm's speed. This modification involves adjusting the commonly used optimization algorithm by adding a check for Armijo-Goldstein conditions when updating the wavelet at each iteration. When applied to a high-resolution seismic dataset, the modified algorithm produces a wavelet that is very similar to the one obtained from seafloor stacking.
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Impact of Geochemical Properties and Occurrence Depth of Central Indian Coal Seams on their CBM Content
More LessSummaryCBM is an unconventional form of energy associated with coal as a by-product, which is developed during its formation and has composition similar to natural gas containing mostly methane. Due to lower carbon dioxide emission and high heat value on combustion, it is a superior fuel compared to coal and considered as a promising source of unconventional energy. India has the world’s fifth largest coal deposits, where its economy is also primarily dependent on coal for energy. The Indian coalfields consisted of Gondwana sediments, which are the potential storehouse of Coalbed Methane. Till date 16613km2 area has been identified for CBM exploration having prognosticated resource of about 62.4Tf3. Indian coal industries have highly committed towards CBM exploration, where around 9.9Tf3 CBM reserve has been explored as Gas in Place (GIP). The gas content of coal seams depends on several factors including its quality, occurrence depth, insitu conditions (temperature and pressure), maturity and permeability. The Indian coalfields mostly consists of bituminous to sub-bituminous coal, which are considered to potential storehouse of CBM. The present paper aims to study the dependence of gas content of major coal seams of Sohagpur Coalfield with respect to its quality, maturity and occurrence depth.
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Mapping of Subsurface Features Responsible for Fracturing of View Point Patch at Dipka opencast Coal Mine
More LessSummaryWith an annual production and dispatch of about 40Mt and 36.9Mt, Dipka opencast coalmine is considered as a mega project globally. The aforesaid mine plays a significant role towards coal supply for Indian economy. Dipka opencast mine follows the conventional blasting method for removal of overburden, whereas coal is excavated through surface miners for minimizing its contamination from dirt bands. The present paper attempts to investigate the insitu sources of fractures, which developed in the Old View Point Patch (bench) during blasting in the month of March’2023. The patch is situated along the north eastern part of the project, where the fractures were first reported by the slope stability radar during blasting in the month of March (2023). Mining operations were immediately put on hold in order to avoid any accidents and urgent investigations were summoned to identify the potential sources of fractures. Since, the opencast mine under investigation played a very curtail role in coal supply, which forms a major source for country’s primary energy. Thus, the present paper investigates the position of subsurface discontinuities through integrated study of Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) and correlates with the position of existing faults.
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Numerical Simulation of Hydrofracturing for CBM Development/Production over Lower Permeable Mature Coal seams of Sohagpur Coalfield
More LessSummaryCoal seams are often associated with trapped methane and some of major Indian coalfields including Sohagpur Coalfield, Jharia Coalfield and Sonhat Coalfield are potential storehouses of CBM. On the basis of methane concentration and its emission, the DGMS has identified the Indian coal seams as Degree I, Degree II and Degree III category, where Degree III seams have highest methane content. In India, around 342 coal mines are currently operational in gassy coal seams, where 95 coal mines are working in the Degree II and Degree III coal seams. Methane is a highly inflammable gas and its concentration in underground mines must be less than 5% for avoiding gas explosions. Hence, degasification is very essential for reducing the risks of gas contamination and explosion for enhancing safety in coal mines. Hydrofracturing is a perforation method used for enhancing the CBM recovery from lower permeable coal seams. The effect of hydrofracturing on coal seams depends on its rank, cleat spacing, fracture density etc. which effects the penetration depth in a wide variation (2.5–60m). The present paper aims to understand the physical as well as mechanical changes due to hydrofracturing in the low permeable high rank bituminous coal seams of Sohagpur Coalfield.
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Experimental and Numerical Simulation of Foam for Co-optimizing the Methods of Oil Recovery and CO2 Storage
More LessAuthors A. Bello, D.B. Dorhjie, A. Ivanova and A. CheremisinSummaryThrough a multiscale approach, this study highlights the importance of controlling CO2 mobility for the effective subsurface use and sequestration of anthropogenic CO2 in depleted formations, which not only improves oil recovery but also increases CO2 storage efficiency—an essential step toward achieving a zero-carbon economy. In this study, novel techniques were developed by injecting CO2 foam generated with a nonionic-based binary surfactant system to improve geological CO2 storage and to co-optimize carbon utilization and storage efficiency in high salinity carbonate porous media, based on hypotheses from our previous works. This study is significant for its potential to simultaneously reduce greenhouse gas emissions and enhance oil production, presenting a sustainable technique for the petroleum industry. The findings of this work are particularly valuable in the context of the Intergovernmental Panel on Climate Change (IPCC)’s decarbonization strategy, which aims to limit global warming to between 1.5 and 2°C.
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FMI Signature of the Low Resistivity Pay (LRP) in Lower Acacus Reservoir, Northern Ghadames Basin-NW Libya
More LessAuthors A. Elmasli and I. MohamedSummaryThe Lower Acacus reservoir is characterized by frequent alternation of sandstone, siltstone and shale. The vertical association of these rocks highly influences the reservoir quality and its lateral continuity. Therefore, the reservoir succession shows variation in the depositional pattern sequence from coarsening to fining upwards, as well as changes in the sedimentary structures and consequently disparity in the nature of paleo-flow. Integration of core image and FMI analysis assisted in the recognition of four main facies within the Lower Acacus reservoir; these are heterolithic bedding, cross-bedded sandstone, shaly-sand, and laminated shales. The association of these facies reflects a clear tidal effect on the deltaic deposition as supported by paleocurrent direction analysis. Moreover, the reservoir lithology is dominated by an argillaceous sandstone interbedded with shale and siltstone beds. The presence of clays and conductive minerals in the sand units, along with thinly laminated sand-shale units, creates difficulty in the hydrocarbon evaluation mainly because of the effect of those materials on resistivity measurements. This phenomenon is known as the Law Resistivity Pay (LRP), which is clearly exhibited by Lower Acacus shaly-sand reservoirs in the Ghadamis Basin.
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Energy-Flux Velocity Vector and Complex Snell’s Law-Based Ray Tracing in Layered Viscoelastic Anisotropic Materials
More LessSummaryIn this study, we derive a complex energy-flux velocity vector that remains homogeneous for any slowness vector, including those associated with post-critical incidences. This derivation is based on time-averaged, real-valued energy velocity vectors and Q-factors. The proposed energy-flux velocity vector enables the application of complex Snell’s law at interfaces, extends ray tracing to post-critical incidences, and facilitates efficient ray tracing without requiring stationary slowness vectors. Numerical examples demonstrate that the energy flux velocities of scattering waves, particularly qSV waves, align well with ray velocities calculated using RSL and RSD methods at the interfaces of viscoelastic anisotropic materials, except in post-critical incidences, where RRT methods fall short. The feasibility of using energy flux velocity for ray tracing is illustrated through qSV wave ray tracing in a layered VTI model, with calculated ray paths and both real and imaginary travel times. The resulting triplications lead to significant changes in ray directions, consistent with previous studies.
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Integrated Seismic Modelling and Imaging of Physical Models
More LessAuthors C. Willacy and T.P. DooleySummaryPhysical models are scaled representations of geological systems that enable greater understanding of structural deformation including salt tectonics. These models have been integrated into geophysical workflows for the creation of synthetic seismic forward modelling and imaging, by extraction of the physical model details and incorporation within an elastic earth model. This is achieved by utilizing digital photo imagery of model vertical cross-sections and using the pixel intensities and layer colours to provide detailed contrasts in seismic impedance. Seismic modelling and imaging can then be applied at field scales. This workflow is demonstrated on three case examples ranging from basin, prospect to reservoir scales. The resultant seismic data contains all the wealth of detail that is present in the original physical models and represents a realistic environment for testing of geophysical workflows including, modelling, imaging, processing, acquisition survey design and quantitative interpretation.
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A New Paradigm of Rapid Seismic Interpretation Powered by Pretrained Seismic Foundation Models
More LessSummaryDue to its strong dependency on data quality and domain knowledge, seismic interpretation remains to be one of the most challenging tasks in subsurface characterization and significant progress is anticipated in automation and efficiency improvement. While in recent years, deep learning (DL) has provided solutions to various seismic interpretation challenges, particularly fault detection and horizon picking, each of the existing DL-based algorithms is defined for completing its specific task only and requires repeated training of a model whenever there is changes in data and/or labels. Another major drawback is their failure to integrate with models from non-seismic domains for developing multidisciplinary applications. Resolving both challenges becomes feasible through pretraining a seismic foundation model (SFM) that is aware of common seismic patterns and generalizes well across different seismic surveys. Correspondingly, pretrained SFMs are considered to be of great potential in revolving the way seismic interpretation can be more automated and efficient. This paper presents four scenarios that leverages a SFM for rapid seismic interpretation and perform a test using various cases, including seismic data interpolation, interpretation, inversion, and textual description.
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Two-Term and Three-Term AVO Approximation for Facies Classification
More LessAuthors L. De Figueiredo and D. GranaSummarySeismic facies classification is generally performed in the domain of elastic attributes, such as P-impedance and P- and S- wave velocity ratio. The standard reservoir characterization workflows compute elastic properties from seismic data using seismic inversion methods based on Aki-Richards approximation, calculate the attributes of interest, and perform the facies classification in the selected domain. In this work, we propose a new AVO approximation formulated in the domain of the elastic properties used for the facies classification. We present a two-term AVO approximation in terms of P-impedance and P- and S- wave velocity ratio and a three-term AVO approximation in terms of P-impedance, P- and S- wave velocity ratio, and density. The formulations can be derived by combining the traditional formulations with rock physics relations, such as Gardner’s equation. We demonstrate the accuracy of the proposed equations by comparing them to the existing models for different reflectivity scenarios in two different geological settings.
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Comparison of Data- and Image- Domain Linearized Inversion via Velocity-Impedance Parametrization
More LessSummaryLinearized waveform inversion, also known as least-squares reverse time migration (LSRTM), is one of the classic seismic imaging methods to reconstruct model perturbations within a known reference medium. It can be computed in either data or image domain using different methods by solving a linear inverse problem, whereas a careful comparison analysis of them is lacking in the literature. In this article, we present a comparative study for multiparameter LSRTM in data- and image- domain under In Vp-ln Ip parameterization. The data-domain LSRTM gives a quantitative estimation of the perturbation of models of higher resolution than image-domain LSRTM, whilst the higher computational cost. We discovered that image-domain
LSRTM suffers crosstalk difficulties in multiparameter linearized inversion, but may work for a single impedance parameter whose PSFs focus locally.
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The Imageability of Salt Inclusions and Caverns for Safe Hydrogen Storage
More LessAuthors C. Willacy, T.P. Dooley, L. Moscardelli and K. De BorstSummaryStoring hydrogen in salt caverns at scale is one potential solution for both short and long-term storage of renewable energy sources, via transformation to and reuse of hydrogen as an energy carrier. To develop robust measurement, monitoring and verification planning for salt cavern storage, the detectability of salt inclusions and caverns must be assessed. However, many legacy seismic surveys have not been designed for detailed internal salt imaging and have source and receiver sampling that is too coarse. To assess the imageability with salt bodies a detailed numerical model has been constructed which has been constrained by both physical modelling and sonar profiles from actual gas storage caverns. Wave equation forward modelling and imaging has been applied to test the imaging limits and to provide guidance to monitoring schemes. In addition, the detection of engineered H2 migration out of the caverns is evaluated and quantified with 4D time-lapse seismic simulations. The results show that well sampled seismic surveys either from surface or downhole should be able to image these features at typical seismic frequencies in most cases, but that this will require higher trace densities that is available from legacy datasets.
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Applying Seismic-Sedimentology and Edge Detection for Determining the Major Sediment-Supply Direction of Progradational Systems
More LessSummaryThe conventional method for determining the major sediment-supply direction(MSSD) using seismic data is to characterize the seismic progradational reflection configuration, which is based mainly on qualitative observation of numerous seismic profiles,and this method is inefficient. In this study, a seismic-sedimentology and edge detection workflow was implemented to determine the MSSD and provide an example analysis from Cambrian carbonate platform-basin system in Tarim Basin, China. Interpretation of −90° phase seismic data and frequency-fused stratal slices helped recognize seismic progradational sequences. By edge detection of stratal slice image, the edge contour of seismic progradational sequences can be obtained, and the maximum curvature of the contour is calculated to obtain the MSSD of progradational systems. Application of the new method provides a suitable example with five steps of workflow to analyze the MSSD, quantifying the main sediment transport direction of the sedimentary system, not only directly supplements the new evidence for the determination of MSSD, but also exerts more geologic thinking in the prediction of MSSD,also be an effective tool for the identification of sedimentary units and of stage analysis for overlapped compound sedimentary units. May have more effective value in imaging the progradational stages and representing the architectural elements of depositional environment.
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Fracture Prediction Technique Based on Pre-Stack and Post-Stack Multi-Attribute Fusion
More LessSummaryTo improve the accuracy of fracture prediction, we propose a fracture prediction method based on the fusion of pre-stack and post-stack seismic attributes. First, by analyzing the tectonic background, we establish fracture identification principles suitable for the geological context. Detailed structural interpretation is conducted based on post-stack seismic data volumes, leading to structural zoning and the analysis and systematic summary of seismic response characteristics. This forms a fundamental understanding of the spatial development patterns of fractures. On this basis, various seismic attribute volumes are extracted, including enhanced coherence volumes, maximum likelihood volumes, and curvature attribute volumes, with different sensitivity parameters set for different subareas. Pre-stack anisotropic inversion is performed using multi-azimuth AVO methods to extract pre-stack azimuthal anisotropy intensity and orientation data volumes. Through multi-attribute selection and fusion, fracture attribute identification is optimized, and weight parameters are calibrated based on actual fracture characteristics observed in wells, resulting in an accurate spatial distribution volume of fractures. This integrated method not only enhances the resolution of fracture prediction but also allows the identification of fractures of different scales and characteristics, providing a foundation for further reservoir studies and development.
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Accelerating Marchenko Imaging via Self-Supervised Focusing Function Prediction
More LessAuthors N. Wang, M. Ravasi and T. AlkhalifahSummaryMarchenko redatuming is a powerful tool to retrieve full-wavefield Green’s functions, which can be ultimately used to create images of the subsurface without artifacts caused by internal multiples. However, the calculation of focusing functions is computationally expensive, particularly for large imaging areas. We propose to cast the focusing function estimation process as a self-supervised learning task. More specifically, a U-Net network is trained on a small subset of focusing functions pre-computed using the conventional iterative scheme, and tasked to learn the up-going focusing function from its initial estimate (i.e., standard redatuming). The predicted up-going focusing function is then directly used to calculate the down-going focusing function, as well as the up- and down-going Green’s functions using the Marchenko physical relationships. This approach integrates data-driven predictions with physical constraints to enhance efficiency. Evaluation on a synthetic dataset demonstrates comparable imaging quality to that achieved using the focusing functions estimated using the iterative scheme over the entire imaging domain, while reducing computational time by a factor of ∼6, making it a promising solution for large-scale seismic imaging tasks.
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Deep Learning-Based Seismic Impedance Inversion Method Using a Dual-Branch Architecture Model
More LessSummaryDeep learning (DL) methods have shown significant potential in post-stack seismic impedance inversion, but their effectiveness is often limited by the availability of training data. Using forward modeling to enrich labeled datasets or embedding a forward modeling module within the network can help mitigate this issue. However, these approaches often rely on wavelets or increase training complexity. This paper proposes a dual-branch network architecture for seismic impedance inversion. The model combines multi-sized convolutional kernels for feature extraction and employs a self-attention mechanism to capture relationships between different sampling points. The results of local and global feature extraction are fused in real time, ensuring that detailed information is preserved while global patterns are learned. Additionally, the initial impedance model is used as an input to constrain the training process. This dual-branch structure enables the model to fully learn the nonlinear relationship between seismic data and impedance without relying on seismic wavelets or a forward modeling module. Experiments on synthetic and field data demonstrate that the proposed method accurately predicts impedance with limited samples, effectively delineating complex geological structures.
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Seal Characterization for Containment Integrity in Saline Aquifer as Potential Storage Site for CCS
More LessAuthors A. WidyanitaSummaryPorosity and permeability values are determined based on readings obtained through He-Pycnometry. Based on the core description, three lithofacies mudstone groups identified, which are massive mudstone, heterolithic/laminated mudstone, and bioturbated mudstone. These covers the EOD ranging from LCP, LCST, TDL, ESTR, DLT and MN. A total of 35 sample measurements are taken, each representing distinct lithofacies types, EOD, and stratigraphic sequences within the entire basin. The porosity values exhibit a depth-related trend, with deeper samples generally having lower porosity. Conversely, permeability values may not be influenced by depth, with potential impacts from factors such as pore throat distribution, mineral composition, clay content, and overall connectivity. This initial screening of the shale samples serves as an assessment of containment integrity and overall quality. Typically, deeper intervals tend to exhibit lower porosity, although there may be variations in permeability ( Figure 3 ).
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Self-supervised Deep Learning Framework for Multi-Source Full Wave Inversion
More LessAuthors O. M. Saad and T. AlkhalifahSummaryFull Wave Inversion (FWI) is an effective tool for estimating subsurface velocity models; however, its high nonlinearity presents certain limitations, including significant computational costs in terms of time and hardware resources. Accelerating the FWI inversion process without compromising performance remains a challenging task. To address this issue, we propose the FreqSiameseFWI framework, a deep learning-based misfit function designed to expedite the FWI inversion process and support Multi-source FWI (MSFWI) by mitigating the impact of cross-talk noise. FreqSiameseFWI employs a self-supervised learning approach integrated within the FWI framework, enabling iterative updates of its parameters without introducing significant additional overhead costs. The seismic data are converted to the frequency domain through the Fast Fourier Transform (FFT), allowing the Siamese network to extract spectral features from the seismic data and achieve robust inversion performance. The proposed FreqSiameseFWI effectively mitigates the influence of cross-talk noise. The performance of FreqSiameseFWI, evaluated using the Overthrust model, has yielded promising results that outperform conventional misfit functions.
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Estimating Fracture and Fluid Indicators Using Model Constraints Extracted from Seismic Attributes
More LessSummaryInversion of seismic amplitude variation with incident angle and azimuth (AVAZ) data for estimating fracture and fluid indicators is an important task. We first express reflection coefficient and azimuthal elastic impedance (AEI) in terms of fracture and fluid indicators, and based on the reflection coefficient and AEI, we proposed a two-stage inversion method of using azimuthal seismic data to estimate fracture and fluid indicators. To improve the accuracy of multi-parameter inversion, we consider establishing good initial models of multiple unknown parameters to constrain the inversion. We propose a novel method of employing pre-stacked seismic attributes (AVA intercept, isotropic and anisotropic gradients) to obtain initial models of fracture and fluid indicators. To further improve the multi-parameter inversion, we propose to employ Hessian matrix calculated using the first- and second-order derivatives of AEI to compute the perturbation in unknown parameter vector in the second stage of the proposed inversion method. We finally apply the proposed method to noisy synthetic and real data, which verifies that the proposed inversion method is robust and is capable of generating reliable fracture and fluid indicators that match well logging data. We conclude the proposed inversion method is a promising tool for predicting potentially hydrocarbon-bearing fractured reservoirs.
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A High-Performance Numerical Simulation Method for Acoustic Remote Detection Imaging in Fractured Reservoirs
More LessSummaryAcoustic remote detection technology plays a pivotal role in the efficient exploration and development of fractured reservoirs. This technology significantly enhances the accuracy of identifying complex underground geological structures, bringing remarkable benefits to increasing oil and gas production. Nonetheless, the considerable computational demands and the inefficiency in the 3D elastic wave time-domain FD method have impeded extensive adoption within the realm of acoustic remote detection imaging. This study introduces a high-performance technique for three-dimensional elastic wave simulation, which employs a synchronous CPU+GPU heterogeneous parallelism architecture. An optimized dimensionality reduction strategy and 3D-Hbrid-PML absorption boundary have been implemented, significantly elevating the computational efficiency for 3D simulations, thereby enabling the simulation of large-scale models with fracture. The method’s stability, efficiency and precision were verified by conducting numerical simulations on the fracture model. The results indicate that the morphology and orientation of the fracture can be accurately identified by the simulation data.
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