Catálogo de publicaciones - revistas

<jats:p> SEG Innovation Advisor Ellie Ardakani writes about the new SEG Community — an online space to discover opportunities, connect with others who share a passion for geophysics, and create a meaningful impact on the future of SEG. </jats:p>

<jats:p> The science of modern seismology was born more than 100 years ago (1889) when the first teleseismic record was identified and the seismograph was developed ( Ben-Menahem, 1995 ). In 1921, earth exploration was revolutionized when a team led by Clarence Karcher conducted the first field tests of the reflection seismograph in Oklahoma City ( Dragoset, 2005 ). That experiment showed that the subsurface can be imaged using seismic data. Businesses boomed as the seismic method started establishing its track record in finding hydrocarbons. Over the last century, the seismic method has emerged as the cornerstone of exploration geophysics, providing us with increasingly accurate characterizations of the subsurface and enabling us to better discover and describe hydrocarbon prospects, geothermal anomalies, seafloor hazards, aquifers, and much more. </jats:p>

<jats:p> The high resolving power of seismic measurements has promoted wide adoption of the seismic method in oil and gas and other industries. Studying the evolution of seismic resolution, the different factors affecting it, and the remaining barriers enables an improved understanding of where we are today and what lies ahead. The need to improve seismic resolution is best framed in the context of the interpretation questions being raised and the project stage (e.g., new frontier, appraisal, development, or production). Improvements in resolution do not depend on a single aspect of the seismic workflow but on multiple interconnected components including acquisition, processing, imaging, and interpretation methods and technologies. This paper highlights some of the key milestones in improving seismic resolution. We also conjecture on progress likely to be made in the years ahead and remaining opportunities to enhance seismic resolution. </jats:p>

<jats:p> In recent years, the development of time-lag full-waveform inversion (FWI) has enabled the use of the full wavefield (primary reflections, diving waves, and their multiples and ghosts) in the inversion process. With this advancement, it is possible to obtain a very detailed velocity model, ultimately reaching the point of deriving from the velocity a migration-like reflectivity image called the FWI image. When the FWI maximum frequency is increased, high-resolution velocity models are obtained, revealing superior reservoir information compared to conventional imaging results. Two case studies will be discussed in this paper. The first is in the Greater Castberg area where the 150 Hz FWI image greatly surpassed the Q Kirchhoff prestack depth migration image from the water-bottom level down to the reservoir (located at a depth of about 1.5 km). The second case study is over the Nordkapp Basin. The use of the full wavefield for the shallow ultra-high-resolution (UHR) FWI image (run at 200 Hz) revealed reverse faulting and pockmark details that were invisible with Kirchhoff prestack depth migration and reverse time migration. By using additional information present in multiples, ghosts, and diving waves, a spatial resolution down to 3 m was achieved. This made it possible to image very thin features without the need for a dedicated high-resolution acquisition design. The current UHR FWI image workflow provides velocity and reflectivity information in the near surface that is important in identifying optimal locations for various purposes such as well placement and wind-turbine installation. </jats:p>

<jats:p> Although the resolution of a seismic image is ultimately bound by the spatial and temporal sampling of the acquired seismic data, the seismic images obtained through conventional imaging methods normally fall very short of this limit. Conventional seismic imaging methods take a piecemeal approach to imaging problems with many steps designed in preprocessing, velocity model building, migration, and postprocessing to solve one or a few specific issues at each step. The inefficacies of each step and the disconnects between them lead to various issues such as velocity errors, residual noise and multiples, illumination holes, and migration swings that prevent conventional imaging methods from obtaining a high-resolution image with good signal-to-noise (S/N) and well-focused details. In contrast, full-waveform inversion (FWI) imaging models and uses the full-wavefield data including primaries and multiples and reflection and transmission waves to iteratively invert for the velocity and reflectivity in one go. It is a systemic approach to address imaging issues. FWI imaging has proven to be a superior method over conventional imaging methods because it provides seismic images with greatly improved illumination, S/N, focusing, and resolution. We demonstrate with a towed-streamer data set and an ocean-bottom-node (OBN) data set that FWI imaging with a frequency close to the temporal resolution limit of seismic data (100 Hz or higher) can provide seismic images with unprecedented resolution from the acquired seismic data. This has been impossible to achieve with conventional imaging methods. Moreover, incorporating more accurate physics into FWI imaging (e.g., upgrading the modeling engine from acoustic to elastic) can further improve the seismic resolution substantially. Elastic FWI imaging can further reduce the mismatch between modeled and recorded data, especially around bodies of large impedance contrast such as salt. It appreciably improves the S/N and resolution of the inverted images. We show with an OBN data set in the Gulf of Mexico that elastic FWI imaging further improves the resolution of salt models and subsalt images over its acoustic counterpart. </jats:p>

<jats:p> Seismic processing and imaging workflows have been refined over many decades to attenuate aspects of the recorded wavefield which would be improperly mapped into the image domain by legacy migration algorithms such as Kirchhoff prestack depth migration. These workflows, which include techniques such as deghosting, designature, demultiple, and regularization, have become increasingly complex and time-consuming due to the sequential fashion in which they must be tested and applied. The single-scattering (primary-only) preprocessed data are then migrated and used in extensive model building workflows, including reflection residual moveout tomography, to refine low-frequency subsurface models. Obtaining optimal results at each stage requires subjective assessment of a wide range of parameter tests. Results can be highly variable, with different decisions resulting in very different outcomes. Such workflows mean that projects may take many months or even years. Full-waveform inversion (FWI) imaging offers an alternative philosophy to this conventional approach. FWI imaging is a least-squares multiscattering algorithm that uses the raw field data (transmitted and reflected arrivals as well as their multiples and ghosts) to determine many different subsurface parameters, including reflectivity. Because this approach uses the full wavefield, the subsurface is sampled more completely during the inversion. Here, we demonstrate the application of a novel multiparameter FWI imaging technique to generate high-resolution amplitude variation with angle reflectivity simultaneously with other model parameters, such as velocity and anisotropy, directly from the raw field data. Given that these results are obtained faster than the conventional workflow with a higher resolution, improved illumination, and reduced noise, we highlight the potential of multiparameter FWI imaging to supersede the conventional workflow. </jats:p>

<jats:p> Spectral extrapolation is a bandwidth extension technique that we implement by combining spectral inversion with constraints, time-variant wavelet extraction, and targeted broadband filtering. We explain the principles of spectral extrapolation as a valid and effective bandwidth extension method and demonstrate its application to a 2D onshore Philippines legacy seismic data set using a time-variant wavelet extraction, resulting in a tripling of the frequency range of the spectrum. The results indicate significant potential for mapping complex stratigraphy and geomorphological features not evident on the input seismic data images, yielding information about reservoir distribution and connectivity that is often critical for optimal well placement. </jats:p>

<jats:p> Various aspects of survey design have a profound impact on how noise appears on the coherent signal of interest, thus impacting conventional inversion methods in complex environments. We propose a multistage physics-driven prior-based processing technique that is versatile and can be used in a wide range of inversion-based processing applications such as source separation and/or interpolation for any acquisition environments (e.g., land, marine, and ocean-bottom nodes). The inversion-based multistage approach progressively builds the coherent signal model while eliminating the aliasing, blending, and background noise in a signal-safe manner. To stabilize the inversion process, we include physics-driven priors in the multiple stage process, which enhances the sparsity of the coherent signal in the transform domain. Results using real data from land and ocean-bottom node surveys validate the potential of the proposed approach to produce optimal processing results while dealing with the common geophysical challenges related to different seismic acquisitions. </jats:p>

<jats:p> All exploration and production projects, whether for oil-and-gas, mining, or clean-technology applications, begin with an accurate image of the subsurface. Many technologies have been developed to enable the acquisition of cost-effective seismic data, with high-density land seismic programs becoming commonplace. However, as the industry progresses and the long-term surface footprint associated with these programs becomes better understood, new methods are needed to reduce the environmental impact of seismic data acquisition while maintaining sufficient subsurface resolution for accurate resource development. New acquisition geometries are typically easier to create than test in the field due to the high cost of field acquisition and processing. However, by using existing data acquired in a grid, one can decimate the original data set into multiple geometries and process them. This provides an opportunity to fully test new geometries without the expense of field acquisition. In this paper, we present processing, interpretation, and inversion tests from an existing ultra-high-density oil-sands seismic data set decimated based on ecologically improved program designs. We then measure and compare the results to understand the impact of these geometries on subsurface resolution. </jats:p>

<jats:p> Geologic carbon storage represents one of the few truly scalable technologies capable of reducing the CO<jats:sub>2</jats:sub> concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO<jats:sub>2</jats:sub> remains within the storage complex. Among the different monitoring modalities, seismic imaging stands out due to its ability to attain high-resolution and high-fidelity images. However, these superior features come at prohibitive costs and time-intensive efforts that potentially render extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology in which time-lapse images are created by inverting nonreplicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high-fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of realistic synthetic noisy time-lapse seismic data sets are simulated that contain imprints of regular CO<jats:sub>2</jats:sub> plumes and irregular plumes that leak. These time-lapse data sets are subsequently inverted to produce time-lapse difference images that are used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO<jats:sub>2</jats:sub> leakage automatically on unseen data with reasonable accuracy. We consider the use of this classifier as a first step in the development of an automatic workflow designed to handle the large number of continuously monitored CO<jats:sub>2</jats:sub> injection sites needed to help combat climate change. </jats:p>