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Collaborating Authors
Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g.,for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.
- North America > United States > Illinois > Madison County (0.34)
- Europe > United Kingdom > North Sea (0.29)
- Europe > Norway > North Sea (0.29)
- North America > United States > Colorado (0.28)
LITHOCODIUM MOUND IDENTIFICATION USING LWD IMAGE LOG AND QUANTIFIED CUTTING ANALYSIS VALIDATION WITH ANALOGUES
Perrin, Christian (North Oil Company) | Pointer, Chay (North Oil Company) | Al-Mohannadi, Ghada (North Oil Company) | Sen, Shantanu (North Oil Company) | Buraimoh, Muse Ajadi (QatarEnergy)
Lithocodium mounds are early Cretaceous sedimentary structures described in the literature from outcrops, however, never described in the subsurface. The objective of this work is to identify and characterize Lithocodium mounds in the subsurface along a 25,000ft horizontal well. Drill cuttings sampled at a 100ft interval are observed in thin sections to define and quantify key sedimentary indicators (bioclasts, facies, and texture). Logging-while-drilling (LWD) GR, density, neutron, and resistivity logs are acquired along with the LWD high-resolution borehole image (BHI) log. Bedding dips from BHI data, interpreted along the horizontal well, enabled the reconstruction of the reservoir paleotopography. In particular, the alternation of dip azimuth combined with the facies interpretation from the thin sections supported the interpretation of eight distinct mound structures. An assessment of their overall geometry confirmed the mound shape to be subcircular, consistent with the subcircular geometries observed in Oman at the outcrop. The inferred dimensions of the mounds are comparable with the Aptian Lithocodium mounds in Oman (3040m), and their intermound organization resembles that of the Albian mounds in Texas. This work demonstrates the value of analyzing cuttings to complement image log interpretation and the value of outcrop analogs for interpreting sedimentary structures. For the first time, the subsurface identification and characterization of Lithocodium mounds and intermounds are achieved.
- North America > United States > Texas (0.48)
- Asia > Middle East > Oman (0.45)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Sedimentary Geology > Depositional Environment (0.93)
- Geology > Geological Subdiscipline > Stratigraphy (0.66)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.48)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Elastic Full Waveform Inversion (EFWI) is a process used to estimate subsurface properties by fitting seismic data while satisfying wave propagation physics. The problem is formulated as a least squares data fitting minimization problem with two sets of constraints: PDE constraints governing elastic wave propagation and physical model constraints implementing prior information. The Alternating Direction Method of Multipliers (ADMM) is used to solve the problem, resulting in iterative algorithm with well-conditioned subproblems. Although wavefield reconstruction is the most challenging part of the iteration, sparse linear algebra techniques can be used for moderate-sized problems and frequency domain formulations. The Hessian matrix is blocky with diagonal blocks, making model updates fast. Gradient ascent is used to update Lagrange multipliers by summing PDE violations. Various numerical examples are used to investigate algorithmic components, including model parameterizations, physical model constraints, the role of the Hessian matrix in suppressing interparameter cross-talk, computational efficiency with the source sketching method, and the effect of noise and near surface effects.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
Least-squares reverse-time migration of simultaneous source with deep-learning-based denoising
Wu, Bo (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Yao, Gang (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Ma, Xiao (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Chen, Hanming (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Wu, Di (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Cao, Jingjie (Hebei Geo University)
Least-squares reverse-time migration (LSRTM) is currently one of the most advanced migration imaging techniques in the field of geophysics. It utilizes least-squares inversion to fit the observed data, resulting in high-resolution imaging results with more accurate amplitudes and better illumination compensation than conventional reverse-time migration (RTM). However, noise in the observed data and the Born approximation forward operator can result in high-wavenumber artifacts in the final imaging results. Moreover, iteratively solving LSRTM leads to one or two orders of computational cost higher than conventional RTM, making it challenging to apply extensively in industrial applications. Simultaneous source acquisition technology can reduce the computational cost of LSRTM by reducing the number of wavefield simulations. However, this technique can also cause high-wavenumber crosstalk artifacts in the migration results. To effectively remove the high-wavenumber artifacts caused by these mentioned issues, in this paper, we combine simultaneous source and deep-learning to speed up LSRTM, as well as, to suppress high-wavenumber artifacts. A deep-residual neural network (DR-Unet) is trained with synthetic samples, which are generated by adding field noise to synthesized noise-free migration images. Then, the trained DR-Unet is applied on the gradient of LSRTM to remove high-wavenumber artifacts in each iteration. Compared to directly applying DR-Unet denoising to LSRTM results, embedding DR-Unet denoising into the inversion process can better preserve weak reflectors and improve denoising effects. Finally, we tested the proposed LSRTM method on two synthetic datasets and a land dataset. The tests demonstrate that the proposed method can effectively remove high-wavenumber artifacts, improve imaging results, and accelerate convergence speed.
Pseudo-Helmholtz decomposition for elastic VTI wavefield based on wavefront phase direction#xD;#xD;
Yao, Gang (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Fang, Xinyu (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Zheng, Qingqing (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Wu, Di (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Niu, Fenglin (Rice University)
P- and S-waves are coupled when propagating in anisotropic elastic media. The separation of P- and S-waves helps study the characteristics of different types of seismic waves, as well as, mitigating crosstalk artifacts in elastic reverse-time migration and elastic full-waveform inversion. At present, the methods of seismic wave mode separation in anisotropic media mainly are built on curl-like and divergence-like operations, pseudo-Helmholtz decomposition, and low-rank approximation. We propose a new pseudo-Helmholtz decomposition operator based on eigenform analysis and the wavefront phase direction to decompose VTI elastic wavefields. The corresponding P/S-wave decoupling formulas are also derived in detail. Compared with the divergence-like and curl-like methods, the new method does not change the phase of P- and S-waves. Compared with existing pseudo-Helmholtz decomposition methods based on eigenform analysis, the proposed method achieves more accurate wavefield separation than the zero-order pseudo-Helmholtz decomposition operator. The proposed method requires solving one vector Poisson equation only, resulting in much less computational cost than the existing first-order pseudo-Helmholtz decomposition methods. In addition, the accuracy of the proposed method was analyzed by providing homogeneous media with different parameter settings. Finally, the numerical examples demonstrate that the new pseudo-Helmholtz decomposition method is effective, efficient, and robust against random noise.
- Asia > China (0.69)
- North America > United States > Texas (0.28)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.55)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.34)
In simultaneous-shot full-waveform inversion (FWI), the re-started L-BFGS algorithm can be used to suppress crosstalk, but crosstalk cannot be completely eliminated from the inversion results. To further solve the crosstalk problem caused by the interference among individual shots in simultaneous-shot FWI, an adaptive structure-based smoothing is applied to the FWI with the re-started L-BFGS algorithm. The structure-based smoothing can mitigate the crosstalk by highlighting structure boundaries. To perform structure-based smoothing, an implicit anisotropic diffusion equation is solved. We carry out a multiscale inversion strategy. As the FWI results in the low-frequency band contain less structural information and more crosstalk, structure-based smoothing is applied to the frequency band at frequencies higher than the peak frequency to prevent negative effects from the model structure in the low-frequency band. The estimated structural information is iteratively updated during the inversion process. Furthermore, the structure-based smoothing is only added to the iterations with invariant encoding to reduce over-smoothing. The invariant encoding means that the shot encoding remains unchanged between iterations. Numerical experiments with an overthrust model indicate that the proposed adaptive structure-based FWI method can effectively suppress artifacts and provide a high-resolution inversion result, even when the encoded data is contaminated with strong noise. Another advantage of the adaptive structure-based FWI method is that no seismic migration needs to be performed, which makes it more efficient for real data.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
The virtual event will feature a technical program showcasing the latest in geophysical surveying and data processing for surveys performed on land, in water, and from the air. Besides former military training sites, the summit will address the interest that industry and governments are seeing in parts of the globe where kinetic engagements have been or are currently being fought. The event brings together researchers, industry professionals, and students to share and discuss recent advancements, challenges, and applications of geophysics for solving today's detection, characterization, and removal of ERW. Sponsorship opportunities are available for this highly-anticipated workshop. To sign up as a sponsor, please complete this form and e-mail it to Laurie Whitesell at lwhitesell@seg.org.
ABSTRACT Prismatic reflections in seismic data carry abundant information about subsurface steeply dipping structures, such as salt flanks or near-vertical faults, playing an important role in delineating these structures when effectively used. Conventional linear least-squares reverse time migration (L-LSRTM) fails to use prismatic waves due to the first-order Born approximation, resulting in a blurry image of steep interfaces. We develop a nonlinear LSRTM (NL-LSRTM) method to take advantage of prismatic waves for the detailed characterization of subsurface steeply dipping structures. Compared with current least-squares migration methods of prismatic waves, our NL-LSRTM is nonlinear and thus avoids the challenging extraction of prismatic waves or the prior knowledge of L-LSRTM results. The gradient of NL-LSRTM consists of the primary and prismatic imaging terms, which can accurately project observed primary and prismatic waves into the image domain for the simultaneous depiction of near-horizontal and near-vertical structures. However, we find that the full Hessian-based Newton normal equation has two similar terms, which prompts us to make further comparison between the Newton normal equation and our NL-LSRTM. We determine that the Newton normal equation is problematic when applied to the migration problem because the primary reflections in the seismic records will be incorrectly projected into the image along the prismatic wavepath, resulting in an artifact-contaminated image. In contrast, the nonlinear data-fitting process included in the NL-LSRTM contributes to balancing the amplitudes of primary and prismatic imaging results, thus making NL-LSRTM produce superior images compared with the Newton normal equation. Several numerical tests validate the applicability and robustness of NL-LSRTM for the delineation of steeply dipping structures and illustrate that the imaging results are much better than the conventional L-LSRTM.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
Rock-physics model of a gas hydrate reservoir with mixed occurrence states
Wu, Cun-Zhi (China University of Petroleum (Beijing)) | Zhang, Feng (China University of Petroleum (Beijing)) | Ding, Pin-Bo (China University of Petroleum (Beijing)) | Sun, Peng-Yuan (CNPC, National Engineering Research Center for Oil and Gas Exploration Computer Software) | Cai, Zhi-Guang (CNPC, National Engineering Research Center for Oil and Gas Exploration Computer Software) | Di, Bang-Rang (China University of Petroleum (Beijing))
ABSTRACT Seismic interpretation of gas hydrates requires the assistance of rock physics. Changes in gas hydrate saturation can alter the elastic properties of formations, and this relationship can be considerably influenced by the occurrence state of gas hydrates. Pore-filling, load-bearing, and cementing types are three single gas hydrate occurrence states commonly considered in rock-physics investigations. However, many gas hydrate-bearing formations are observed to have mixed occurrence states, and their rock-physics properties do not fully conform to models of single occurrence states. We develop a generalized rock-physics model for gas hydrate-bearing formations with three mixed occurrence states observed in the field or laboratory experiments: coexisting pore-filling-type and matrix-forming-type gas hydrates (case 1); pore-filling type when (gas hydrate saturation) < (critical saturation) and pore-filling + matrix-forming type when (case 2); and matrix-forming type when and matrix-forming + pore-filling type when (case 3). Instead of initial porosity, the apparent porosity (the volume fraction of an effective pore filler) represents the influence of occurrence states on the pore space. These three mixed occurrence states can be modeled using a unified workflow, in which the volume fractions of various gas hydrate types are expressed in general forms in terms of the apparent porosity. In addition, the model considers the effect of a pore filler on shear modulus. The developed model is validated through calibration with real well-log data and published experimental data corresponding to five gas hydrate-bearing formations. The model effectively interprets the influences of gas hydrate saturation and occurrence state on these formations. Thus, the generalized model provides a theoretical basis for the analysis of sensitive elastic parameters and quantitative interpretation for gas hydrate reservoirs.
- North America > United States (0.46)
- Asia > China (0.29)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- Europe > Norway > Norwegian Sea (0.24)
ABSTRACT Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.