Catálogo de publicaciones - revistas

<jats:p> The Editorial Calendar details upcoming (approximately one year in advance) publication plans for The Leading Edge. This includes all special sections, guest editors, and information about submitting articles to TLE. </jats:p>

<jats:p> President Ken Tubman invited staff to share its perspective as SEG continues its transformation. Having been a member and industry stakeholder leader for many years and executive director (ED) for more than two years now, I am honored to represent this hard-working group of individuals. This invitation led me to consider how people view the Society that we serve as employees. </jats:p>

<jats:p> The estimation of the parameters of a mathematical model by data-fitting procedures goes back more than 200 years. Mathematics historians seem to agree to credit C. F. Gauss for the introduction of this idea ( Gauss, 2011 ). In the field of exploration geophysics, Lailly (1983) and Tarantola (1984) were the first to propose the use of data-fitting techniques to estimate model parameters that control the propagation of waves in the subsurface. The concept of full-waveform inversion (FWI) was born. </jats:p>

<jats:p> Seismic image deterioration is a major problem for structures under complex overburdens. In areas under major faults and salt bodies, it is common to observe amplitude washout zones, poor structural definition, and relatively strong coherent and incoherent seismic noise. We developed a data-driven workflow of Q least-squares migration to mitigate these problems by enhancing the horizontal and vertical resolutions of seismic images. The workflow includes accurate velocity model building with joint full-waveform inversion and tomography to maximize the stacking power of P-wave primaries, Q tomography that balances weak-amplitude washout zones and minimizes swing noises, and least-squares migration with the aid of a constraint map that yields higher-resolution structural images. Its application on a wide-azimuth data set from the Tengiz oil field demonstrates the effective mitigation of fault shadow issues with an overall improvement of image quality. In addition, the uplift in focusing of diffracted energies shows promising improvements in enhancing seismic mega-amplitude events. Thus, the proposed method greatly increases the fidelity of seismic attribute analysis when compared with conventional vintage processing. </jats:p>

<jats:p> The SEG Advanced Modeling (SEAM) Arid benchmark model was designed to simulate an extremely heterogeneous low-velocity near surface (NS), which is typical of desert environments and typically not well characterized or imaged. Imaging of land seismic data is highly sensitive to errors in the NS velocity model. Vertical seismic profiling (VSP) partly alleviates the impact of the NS as the receivers are located at depth in the borehole. Deep learning (DL) offers a flexible optimization framework for full-waveform inversion (FWI), often outperforming typically used optimization methods. We investigate the quality of images that can be obtained from SEAM Arid VSP data by acoustic mini-batch reverse time migration (RTM) and full-waveform imaging. First, we focus on the effects of seismic vibrator and receiver array positioning and imperfect knowledge of the NS model when inverting 2D acoustic data. FWI imaging expectedly and consistently outperforms RTM in our tests. We find that the acquisition density is critical for RTM imaging and less so for FWI, while NS model accuracy is critical for FWI and has less effect on RTM imaging. Distributed acoustic sensing along the full length of the well provides noticeable improvement over a limited aperture array of geophones in imaging deep targets in both RTM and FWI imaging scenarios. Finally, we compare DL-based FWI imaging with inverse scattering RTM using the upgoing wavefield from the original SEAM data. Use of significantly more realistic 3D elastic physics for the simulated data generation and simple 2D acoustic inversion engine makes our inverse problem more realistic. We observe that FWI imaging in this case produces an image with fewer artifacts. </jats:p>

<jats:p> Frequency-domain full-waveform inversion (FWI) is potentially amenable to efficient processing of full-azimuth long-offset stationary-recording seabed acquisition carried out with a sparse layout of ocean-bottom nodes (OBNs) and broadband sources because the inversion can be performed with a few discrete frequencies. However, computing the solution of the forward (boundary-value) problem efficiently in the frequency domain with linear algebra solvers remains a challenge for large computational domains involving tens to hundreds of millions of parameters. We illustrate the feasibility of 3D frequency-domain FWI with a subset of the 2015/2016 Gorgon OBN data set in the North West Shelf, Australia. We solve the forward problem with the massively parallel multifrontal direct solver MUMPS, which includes four key features to reach high computational efficiency: an efficient parallelism combining message-passing interface and multithreading, block low-rank compression, mixed-precision arithmetic, and efficient processing of sparse sources. The Gorgon subdata set involves 650 OBNs that are processed as reciprocal sources and 400,000 sources. Monoparameter FWI for vertical wavespeed is performed in the viscoacoustic vertically transverse isotropic approximation with a classical frequency continuation approach proceeding from a starting frequency of 1.7 Hz to a final frequency of 13 Hz. The target covers an area ranging from 260 km<jats:sup>2</jats:sup> (frequency ≥ 8.5 Hz) to 705 km<jats:sup>2</jats:sup> (frequency ≤ 8.5 Hz) for a maximum depth of 8 km. Compared to the starting model, FWI dramatically improves the reconstruction of the bounding faults of the Gorgon horst at reservoir depths as well as several intrahorst faults and several horizons of the Mungaroo Formation down to a depth of 7 km. Seismic modeling reveals a good kinematic agreement between recorded and simulated data, but amplitude mismatches between the recorded and simulated reflection from the reservoir suggest elastic effects. Therefore, future works involve multiparameter reconstruction for density and attenuation before considering elastic FWI from hydrophone and geophone data. </jats:p>

<jats:p> Waveform inversion aims to retrieve high-resolution earth parameter volumes. Due to its high computational cost, acoustic approaches using low to mid seismic data frequencies are often applied in velocity model building. However, in the presence of large parameter contrasts, an elastic formulation should be preferred due to wave interferences that limit the applicability of phase- and kinematics-only approaches. The benefits of elastic waveform inversion have been demonstrated with inversion of hydrophone data in marine environments. Here, we extend the approach to inversion of the vertical geophone component. We propose a weighted cost function in the waveform inversion algorithm to jointly invert multicomponent data sets, and we compare the results to the inversion of single-component data. For this study, we use an ocean-bottom-node data set from the deepwater Gulf of Mexico around a salt dome. We show that slightly different velocity models, reverse time migration images, and angle gathers are retrieved when using hydrophone or vertical geophone data, further improving either the shallow or deeper sediments. The joint inversion combines the improvements brought by the single-component inversions. Though it doubles the cost, joint elastic waveform inversion of hydrophone and vertical geophone data can help velocity model building around salt bodies. </jats:p>

<jats:p> Velocity model building and imaging for land surveys are often challenging due to near-surface complexity contaminating the reflection signal. Incorporating full-waveform inversion (FWI) in the velocity model building workflow for land surveys offers benefits not achieved with traditional model building tools, but it also brings difficulties. We have developed an effective model building workflow for land seismic data that incorporates dynamic matching FWI (DMFWI). DMFWI employs an objective function that uses multidimensional local windowed crosscorrelations between the dynamically matched version of observed and synthetic data. Dynamic matching de-emphasizes the impact of amplitudes, allowing the algorithm to focus on using kinematic information for velocity updates. The proposed workflow produces a geologically plausible and consistent model for data acquired with limited offsets. Refraction and reflection tomography may also be included in the workflow. The workflow is applied to onshore surveys in Mexico. Despite challenges of the near-surface geology and limitations of the acquisition parameters in the study areas, the proposed model building workflow successfully derives a high-resolution velocity model that significantly improves the migrated depth image. </jats:p>

<jats:p> Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required. </jats:p>

<jats:p> Full-waveform inversion (FWI) has become the centerpiece of velocity model building (VMB) in seismic data processing in recent years. It has proven capable of significantly improving the velocity model and, thus, the migration image for different acquisition types and geologic settings, including complex environments such as salt. With the advent of FWI imaging, the scope of FWI applications has extended further from VMB into the imaging landscape. However, current FWI applications in the industry prevalently employ the acoustic approximation. One common problem of acoustic FWI (A-FWI) is the apparent salt halos at the salt-sediment interface in the resulting FWI velocity and FWI image, the presence of which hinders direct interpretation and imaging focusing around salt bodies. With synthetic and field data examples, we demonstrate that this salt halo is caused mainly by the large mismatch between the elastic recorded data and the acoustic modeled data, particularly at middle to long offsets. To overcome limitations imposed by acoustic assumptions, we developed an elastic FWI (E-FWI) algorithm that combines an elastic modeling engine with the time-lag cost function, which we call elastic time-lag FWI (E-TLFWI). With a more accurate modeling engine, E-TLFWI significantly reduces the salt halo observed in its acoustic counterpart. However, we also observe that the images migrated using the A-FWI and E-FWI velocity models remain similar overall, with some slight improvements around and beneath salt boundaries, particularly near steep salt flanks, as a result of the reduced salt halo. By contrast, FWI images derived from E-TLFWI show considerable benefits over those from acoustic time-lag FWI, such as improved event focusing, better structural continuity, and higher signal-to-noise ratio. The sharpened salt boundaries and enhanced quality of the FWI images reveal the significant value of E-FWI and provide the justification for its greatly increased cost. </jats:p>