Unlocking unprecedented seismic resolution with FWI Imaging

Full-waveform inversion (FWI) has found great success in different geologic settings and has become a must-have tool for velocity model building (VMB), particularly in salt environments where geology and velocity are often highly complex. While still acoustic, FWI has already significantly improved salt models and marked a step change in subsalt imaging compared to conventional VMB workflows driven by manual salt interpretation. Furthermore, the introduction of an innovative imaging method — FWI imaging — has delivered another step change in subsalt imaging, providing subsalt images of greatly improved signal-to-noise ratio (S/N) and better-balanced amplitudes compared to conventional migration methods. Most recently, the evolution of FWI from acoustic (AFWI) to elastic (EFWI) has represented another key breakthrough, yielding velocity models and FWI images with better-defined details and higher S/N, especially in salt provinces where impedance contrasts are large and elastic effects are most pronounced. Our elastic time-lag FWI (E-TLFWI), which integrates an elastic modeling engine with a time-lag cost function, has demonstrated the ability to reduce salt halos, better delineate structures around salt bodies, and enhance subsalt S/N in both velocity models and FWI images. We illustrate these improvements using two ocean-bottom node (OBN) data sets and one streamer data set. Given the proven advantages of EFWI imaging in 3D, it is natural to extend its application from 3D to 4D, particularly for subsalt 4D imaging, which often suffers from weak and imbalanced illumination and poor S/N with conventional 4D processing. The first application of E-TLFWI imaging on 4D data sets over the Atlantis Field in the Gulf of Mexico reveals clearer subsalt 4D signals and more accurate 4D responses that were not previously observed and are confirmed by both well data and production history.

Conventional seismic imaging methods rely on the single-scattering assumption, where only primary reflections contribute to the reflectivity, neglecting the additional illumination provided by multiples. These methods also depend on a velocity model obtained through a separate workflow—such as tomography or full waveform inversion (FWI)—that focuses on recovering the low- to mid-wavenumber components governing wavefield kinematics. On the other hand, FWI imaging takes advantage of the full wavefield to recover both the velocity model and reflectivity image within a single framework. This approach updates low-, mid-, and high-wavenumber components, therefore enhancing image illumination and resolution. In this work, we demonstrate the benefits of this methodology by comparing recent FWI-based imaging results from the Brazil Santos Basin with those from traditional approaches.

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.

This paper presents a case study from the Edvard Grieg field in the North Sea. PP and PS ocean bottom cabled (OBC) seismic data have been used to build high-frequency Vp- and Vs- velocity models using full waveform inversion (FWI). Since the start of production from the field in November 2015, three vintages of time-lapse (4D) OBC data have been acquired, in 2016, 2018 and 2020. In all instances, both PP-and PS- data have been processed and imaged to extract as much 4D information as possible. However, the PS data have been challenging to use in an image sense, as the quality of the images have been low, especially when compared to the PP data. A new PS-FWI method including PS-reflection data has been proposed to update and build a new high-frequency Vs velocity model. This includes both the low-wavenumber background model, as well as the high- frequency perturbation model, to bring the Vs model all the way up to 30Hz. The new 30Hz Vs model combined with a 65Hz Vp model is demonstrated to produce superior images of the reservoir target, both using RTM and FWI derivative image algorithms (FWI Imaging). The method shown here, using processed radial PS data components, is a key milestone in ongoing work to utilize raw PS component data, which will ultimately lead to a full elastic FWI solution.

Velocity model building and depth imaging are known to be complicated endeavors in numerous onshore areas in the Middle East. Despite recent advancements toward denser, wide-azimuth, and low-frequency seismic acquisitions, the application of full-waveform inversion (FWI) remains challenging. The main difficulties arise from complex near-surface geology that creates wave mode conversions (P to S waves but also strong multiples) and very energetic and complex ground roll overlaying reflected waves and dominating low frequencies, with the diving waves being less energetic and noisier. This paper presents recent progress in land FWI toward higher-resolution velocity models, with successful applications to case studies from the Sultanate of Oman. The first example shows an application of land FWI imaging. It shows that we are able to invert jointly diving and reflected waves in acoustic FWI. With proper input data conditioning and initial model preparation, we successfully ran acoustic FWI up to 45 Hz, resulting in a high-resolution velocity model and derived FWI image that significantly improved subsurface imaging and geologic understanding. The inverted high-frequency velocity could also be more reliably used for subsequent reservoir characterization steps. Given the important role of a good-quality near-surface model for stabilizing high-frequency FWI updates, the second example highlights the creation of a detailed shallow velocity model. We propose using elastic FWI of surface waves for characterization of the near surface in complex geologic settings. Here, we demonstrate the effectiveness of this workflow to enhance imaging of the shallow subsurface. Compared to conventional methods such as surface-wave inversion, elastic FWI of surface waves has the advantage of 3D full-wavefield modeling with no requirement for picking. Our workflow also benefits from the ultra-low frequencies recovered by interferometry to obtain a more stable deeper update.

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

Seismic oceanography can provide a two- or three-dimensional view of the water column thermocline structure at a vertical and horizontal resolution from the multi-channel seismic dataset. Several seismic imaging methods and techniques for seismic oceanography have been presented in previous research. In this study, we suggest a new formulation of the frequency-domain reverse-time migration method for seismic oceanography based on the analytic Green's function. For imaging thermocline structures in the water column from the seismic data, our proposed seismic reverse-time migration method uses the analytic Green's function for numerically calculating the forward- and backward-modeled wavefield rather than the wave propagation modeling in the conventional algorithm. The frequency-domain reverse-time migration with analytic Green's function does not require significant computational memory, resources, or a multifrontal direct solver to calculate the migration seismic images as like conventional reverse-time migration. The analytic Green's function in our reverse-time method makes it possible to provide a high-resolution seismic water column image with a meter-scale grid size, consisting of full-band frequency components for a modest cost and in a low-memory environment for computation. Our method was applied to multi-channel seismic data acquired in the Arctic Ocean and successfully constructed water column seismic images containing the oceanographic reflections caused by thermocline structures of the water mass. From the numerical test, we note that the oceanographic reflections of the migrated seismic images reflected the distribution of Arctic waters in a shallow depth and showed good correspondence with the anomalies of measured temperatures and calculated reflection coefficients from each XCDT profile. Our proposed method has been verified for field data application and accuracy of imaging performance.

Full Waveform Inversion (FWI) is a seismic imaging technique that utilizes the entire content of seismic trace. This method aims to minimizes differences between real and synthetic seismograms. FWI consists of three main steps: forward modelling to simulate synthetic seismograms, adjoint simulation to calculate the gradient misfit, and optimization to obtain an appropriate model perturbation. FWI is an extremely non-linear inversion technique and computationally intensive since it involves many model parameters. In the context of geophysics, finding parameter models that describe the structure of the earth’s subsurface is the main goal. Geophysical measurements in the field only obtain data that is influenced by the parameter model, in this case seismic recording data, therefore mathematical techniques are studied and applied to determine the correct parameter model from the data obtained, also known as the inversion process. In this study, main research area is on optimization step which include mathematical approach to find perturbation model effectively. The Newton method can be used in the optimization step of FWI. However, its implementation is not promising due to high computational cost in calculating Hessian matrix. To reduce computation cost, an identity matrix is chosen to replace the Hessian matrix, as it usually done in the steepest descent method. Since FWI has a high level of non-linearity, steepest descent method requires many iterations to minimize the objective function. Therefore, this study uses a conjugate gradient method to make the data misfit minimized more quickly. Apart from that, the Line search technique is used to find an appropriate step-length at each iteration on both methods. The FWI workflow is applied to the synthetic acoustic velocity model. We conduct FWI using steepest descent and conjugate gradient methods. The results show that the conjugate gradient method requires fewer iterations than the steepest descent method to minimize the objective function. FWI model result using conjugate gradient method is closer with the true model compared to steepest descent method, showed on misfit result and model image. By comparing these methods we can see that conjugate gradient method can be chosen for application on FWI optimization step for better result on seismic imaging. On the industry scale, better results provide less uncertainty on the model parameters that useful for energy exploration or engineering.

Defining the root zone of mud volcanoes (MVs), structural interpretation, and geologic modeling of their body is a problematic task when only seismic data are available. We have developed a strategy for integration of gravity and seismic data for better structural interpretation. Our strategy uses the concept of the normalized full gradient (NFG) for integration of gravity and seismic data to define geometry and the root zone of MVs in the southeast onshore of the Caspian Sea. Our strategy will increase the resolution of the seismic envelope compared with the conventional Hilbert transform. Prior to interpretation, we applied the NFG method on field gravity data. First, we perform a forward-modeling step for accurate NFG parameter definition. Second, we estimate the depth of the target, which is the root zone of the MV here. Interpretation of field gravity data by optimized NFG parameters indicates an accurate depth of the root zone. Subsequently, we apply the NFG method with optimized parameters on a 2D seismic data. Application of our strategy on seismic data will enhance resolution of the seismic image. The depth of the root zone and the geometry of the MV and mud flows was interpreted better on the enhanced image. It also illustrates the complex structure of a giant buried MV, which was not well-interpreted on conventional seismic image. Interpretation of the processed data reveals that the giant MV had lost its connection to its reservoir, whereas the other MV is still connected to the mud reservoir. The giant MV is composed of complex bodies due to pulses in the mud flows. Another MV in the section indicates narrow neck with anticline and listric normal faults on its top. Thus, application of the NFG concept on seismic image could be considered as an alternative to obtain enhanced seismic image for geologic interpretation.

Summary Seismic imaging is dependent on the quality of the seismic field record, with geometry playing a crucial role in the integrity of the seismic wave-field. Geometric parameters are therefore considered key in seismic image processing and resolution. Geometries with high density and wide azimuth are shown to improve the seismic image, however, the overhead coverage remains high. Our paper analyzes and compares the effects of varying geometries on the quality of seismic image by constructing a mathematic model from raw data, forward modeling, generating seismic data records of different geometries, processing the seismic data and getting the images of several geometries. The research aims to improve imaging in conditions where complicated structures and rough surface topography typically increase the difficulty in seismic acquisition and data processing. Our test results show that geometries with uniform and symmetrical spatial sampling of both source and receiver can also improve images from a pre-stack migration model and are advantageous in minimizing workload and cost.

Introduction/Objective. The goal of our research was to evaluate diagnostic and prognostic role of positron emission tomography/computed tomography (PET-CT) in patients previously treated for uterine cancer and compare it to conventional imaging methods (CIM). Methods. We analyzed 37 patients examined on PET-CT for follow-up or suspicion of uterine cancer recurrence, and who were previously treated with surgery and/or chemoradiotherapy. All patients underwent CT or magnetic resonance imaging prior to PET-CT, and were followed-up for at least one year. Results. PET-CT showed sensitivity, specificity and diagnostic accuracy in uterine cancer relapse detection of 96.3%, 70%, and 89.2%, while those values for CIM were 92.6%, 40%, and 78.4 %, respectively. Correlation of PET-CT and CIM findings was 78% (29/37). In 13 out of 25 true positive patients on CIM, PET-CT found greater number of active sites missed by conventional imaging. Positive findings on PET-CT were associated with shorter progression free survival (p = 0.023, logrank test). Conclusion. PET-CT constitutes an important diagnostic method in management of recurrent cancer of uterine corpus, demonstrating high sensitivity and accuracy. In comparison to CIM, PET-CT can discover larger number of active tumor sites, and also shows better specificity. PET-CT positive patients have worse prognosis with shorter progression free survival.

Seismic wave attenuation has strong, negative effect on the seismic data resolution. The high influence of this phenomenon was observed during realization of seismic survey Duza Wolka 3D (Geofizyka Krakow S.A. 2010a). The resolution of recorded seismic data was very low due to high attenuation caused by the Lower Triassic Buntsandstein sandstones. In consequence, interpretation of the thin-layered Zechstein formation is ambiguous. The aim of this paper is to present the multi-stage algorithm for the purpose of minimizing the attenuation effect, hence increasing the resolution of the seismic data. In order to achieve this, the inverse Q filtering process was used. The Q factor (quality factor) was estimated from the vertical seismic profiling data (Geofizyka Krakow S.A. 2010c). To verify the effectiveness of inverse Q filtering, seismic modelling was performed. Results received from seismic modelling gave basis for application the inverse Q filtering on real data. For this operation the seismic time profile inline 112 was chosen from the seismic data set Duza Wolka 3D. Application of inverse Q filtering significantly increased the seismic resolution, which enabled the interpretation of the Zechstein formation with more accuracy.

Various approached have been introduced to extract as much as information form seismic image for any specific reservoir or geological study. Modeling of faults and fractures are among the most attracted objects for interpretation in geological study on seismic images that several strategies have been presented for this specific purpose. In this study, we have presented a modified approach of application concept of the principle components analysis to enhance faults and fractures from low quality seismic image. In the first step, relevant attributes considering imaging faults and fractures were have drawn based on vast study on previous successful applications of different attributes. Subsequently, major informative components of each attribute were defined by performing principle component analysis. Since random noise in seismic image exhibits no correlation in seismic data, true reflectors and diffraction events show high coherency value thus these objects would be separated into different orthogonal components in principle component analysis. It will make it easy to remove irrelevant information considering faults and fractures from seismic image and thus will make a higher quality image by combining attribute sections in principle component analysis. Afterwards, selected components were stacked to enhance the fault position in final image. However, since that are other geological objects that might show correlation in other orthogonal components, so there should be refinement step on the final image to stack only the favorable information. This approach was performed on a field land data example form north east of Iran. Result of application the proposed strategy shows that the method is capable to image faults compared to the conventional image analysis for fault detection. The method was also capable to image accurate position of the body of mud volcanoes exited in the image that could not be easily tracked by conventional seismic image analysis.

Summary Full-waveform inversion (FWI) fails to converge when starting with a poor initial model, especially for complex structures such as salt models. Building a good initial salt model requires manual interpretation from seismic images, which is time-consuming and depends on human decisions. Also, for FWI to have a stable convergence, the seismic data should have low frequencies and long offsets to penetrate the deeper structure and build an accurate salt body. Here, we automatically reconstruct the salt body with the aid of deep learning in a 2D vintage field data from the Gulf of Mexico area that lacks frequencies below 6 Hz and have a maximum offset of 4.8 Km. We perform FWI starting from a linearly increasing model in a multiscale approach. We train three 1D U-net networks that are applied subsequently after each FWI frequency scale: the first two U-nets are responsible for salt flooding and improving the top of the salt, and the third one is to unflood the salt to the base. The inversion result reconstructs a large salt body structure, although the data lacks low frequencies and long offsets.

The clarity of seismic images has a great impact on the results of seismic data interpretation; therefore, high-resolution seismic images can provide finer geological information for seismic exploration. Coordinate-based Multilayer Perceptron(MLP) is easy for gradient-based optimization and machine learning, and represents a more compact order of magnitude than grid sampling. In this paper, we propose a combination of Random Fourier Features(RFF) and coordinate based MLP method to achieve super-resolution reconstruction of seismic images, taking one image coordinate as input and the output is the RGB value of the pixel at that location. Preprocessing the spatial coordinates of the image with Fourier feature mapping enables the MLP to learn higher frequency detail information and achieve super-resolution reconstruction of a single seismic image. Experiments show that the texture and edge details of the final processed seismic image are much clearer.