Ocean Bottom Node Data Research Articles

Improved elastic full‐waveform inversion of ocean bottom node data

AbstractElastic full‐waveform inversion enables the quantitative inversion of multiple subsurface parameters, significantly enhancing the interpretation of subsurface lithology. Simultaneously, with the ongoing advancements in ocean bottom node technology, the application of elastic full‐waveform inversion to marine ocean bottom node data is receiving increasing attention. This is attributed to the capability of ocean bottom node to acquire high‐quality four‐component data. However, elastic full‐waveform inversion of ocean bottom node data typically encounters two challenges: First, the presence of low S‐wave velocity layers in the seabed leads to weak energy of converted S‐waves, resulting in significantly poorer inversion results for S‐wave velocity compared to those for P‐wave velocity; second, the cross‐talk effect of multiple parameters further exacerbates the difficulty in inverting S‐wave velocity. To effectively recover the S‐wave velocity using ocean bottom node data, we modify the S‐wave velocity gradient in conventional elastic full‐waveform inversion to alleviate the impact of cross‐talk from multiple parameters on the inversion of S‐wave velocity. Furthermore, to invert for density parameters, we adopt a two‐stage inversion strategy. In the first stage, P‐wave and S‐wave velocities are updated simultaneously with a single‐step length. Because the initial density model is far from the true one, density is updated using an empirical relationship derived from well‐log data. In the second stage, velocities and density are updated simultaneously with multi‐step length to further refine the models obtained in the first stage. The high effectiveness of the improved elastic full‐waveform inversion is validated by numerical examples.

Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal.

Converted-wave reflection traveltime inversion with free-surface multiples for ocean-bottom-node data

Advancements in ocean-bottom-node (OBN) technologies have enabled the recording of high-quality multicomponent seismic data, which can provide crucial information about subsurface properties through elastic seismic imaging. However, imaging multicomponent data is challenging due to the requirement for P- and S-wave velocity models. Conventional processing relies on estimating the S-wave velocity model using a primary converted wave (C wave) because the pure S-wave primaries are not available if using standard air-gun sources. When a free surface is present, the C waves recorded with geophones can be contaminated by source-side P-wave multiples, especially water-layer reverberations, which are difficult to remove even using state-of-the-art wavefield decomposition methods. These C-wave multiples can cause significant mismatches between synthetic and observed data during reflection traveltime or waveform inversion if only primary P-to-S (PS) conversions are simulated. To address these issues and reliably build an S-wave velocity model for PS imaging, we develop a C-wave reflection traveltime inversion method that takes source-side free-surface multiples into account. The traveltime misfit of C-wave data is extracted using dynamic image warping to ensure a better match between the synthetic and observed data. The functional gradient of the objective function is derived using the adjoint state method. Based on sensitivity kernel analyses, a convenient gradient preconditioning strategy is developed to robustly separate the contribution of primary and multiple C waves during backpropagation. This strategy can effectively mitigate the high-wavenumber crosstalk associated with nonphysical wavepaths in the gradient and thus allow more accurate updating for the S-wave velocity model. The synthetic data and shallow-water OBN data from the East China Sea indicate that this approach can reliably recover the S-wave macrovelocity structures for PS imaging.

Atlantis — 20 years of seismic innovation finally removes the shroud of mystery

The Atlantis Field has gone through more than two decades of continuous seismic imaging efforts, during which time many innovative technologies were incubated, the most recent one being the successful application of full-waveform inversion (FWI) in salt environments. This technique led to a significant improvement in the subsalt image. However, imaging challenges remain for the Atlantis reservoirs, primarily due to the complex overburden salt geometries and the highly compartmentalized reservoir. Even with an improved velocity model from FWI, the conventional reverse time migration (RTM) images still suffer from illumination issues and contain strong migration swings that hinder the subsalt imaging and subsequent interpretations. Furthermore, early versions of FWI employed an acoustic assumption, leading to visible salt halos at the salt boundaries in the velocity model, which adversely impacted the reservoir imaging. In the last 12 months, elastic time-lag FWI (TLFWI) and FWI-derived reflectivity (FDR) imaging using long-offset ocean-bottom node data have minimized these imaging issues at Atlantis, providing another step change in subsalt understanding. Although the 3D RTM images using the elastic FWI velocity model are similar overall to their acoustic counterparts, the 4D time-lapse RTM images at Atlantis show noticeable improvements. Furthermore, FDR images derived from elastic FWI velocities show obvious benefits over the acoustic ones. With a more accurate modeling engine that allows for better match between synthetic and real data, FDR imaging shows improved illumination, higher signal-to-noise ratio, and better reservoir details over acoustic FDR imaging. This recent advancement in using elastic TLFWI has had immediate positive effects in facilitating the Atlantis Field's current and future development.

Elastic common-receiver Gaussian beam migration of 4C ocean-bottom node data

Four-component ocean-bottom node (OBN) surveys allow for the imaging of subsurface elastic properties for oil and gas exploration in deepwater environments. However, sparse acquisition sampling for the high-quality imaging of OBN data is challenging. To alleviate this problem, a common-receiver domain 4C elastic Gaussian beam migration method based on elastic reciprocity transformation that considers the monopole/dipole characters of sources and receivers is developed. Common-receiver migration also is computationally efficient in an OBN survey wherein the number of shots usually exceeds the number of geophones. P/S and up-/downgoing wavefield decompositions are accomplished on the “virtual source side” during migration. A decomposition matrix and a wavefield extrapolation formula are derived from the elastic Kirchhoff-Helmholtz integral with the representation of the Green’s function as a superposition of Gaussian beams. The local slant stack is performed on the common-receiver recordings that are subjected to more optimized sampling, which is less sensitive to aliasing. The performance of the method on synthetic data is validated using the coarse sampling of OBNs in a deepwater and ultradeepwater environment.

Deep learning-based attenuation for shear-wave leakage from ocean-bottom node data

The high-amplitude shear wave in the z-component of ocean-bottom node (OBN) data challenges conventional seismic data imaging and interpretation. The process of shear-wave attenuation from the z-component data can be regarded as a coherent noise reduction problem. However, conventional noise attenuation methods, such as filtering or matching and subtraction, are time-consuming or have limited accuracy for field data processing. Unlike conventional methods with indirect feature recognition, pretrained neural networks provide end-to-end denoising frameworks by directly predicting and subtracting the shear-wave-free data from the original z-component data. Here, we have developed a deep learning-based method to address the shear-wave leakage problem and adopted a training set generation strategy based on the field data. Denoised z-component data are not needed to train the network. The generation of the training set and the extraction of noise features are based on the data of the x- and p-components. A residual learning strategy is introduced to extract the noise from the field data other than those in the training set. Synthetic examples confirm the excellent performance of our training set generation strategy. Field 4C OBN examples show that our method can attenuate shear-wave noise without harming the signals and, therefore, can be integrated into multicomponent field data processing.

Is 3D frequency-domain FWI of full-azimuth/long-offset OBN data feasible? The Gorgon data FWI case study

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 km2 (frequency ≥ 8.5 Hz) to 705 km2 (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.

Pushing seismic resolution to the limit with FWI imaging

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.

Automated dispersion curve picking using multi-attribute convolutional-neural-network based machine learning

SUMMARY Surface wave dispersion curves are useful to characterize shallow subsurface structures while accurately picking them is typically laborious. To make these approaches more efficient and practical, it is important to automate the picking process. We propose a convolutional neural network (CNN) based ML method to automatically pick multimode surface wave dispersion curves. We modify the typical U-net architecture to convert the conventional 2-D image segmentation problem into direct multimode curve fitting and subsequent picking. A variety of attributes of the data amplitude (A) in the (f, k) domain, such as frequency (F), wavenumber (K), maximum coherency (Coh) and Power weighted amplitude (Pwa), are combined to constrain the picking more accurately than a single attribute does. The effects of two different loss functions on the final picking results are compared; the one that combines conventional wavenumber residuals and curve slope residuals produces more continuous curves. Pre-training the network with synthetic data, and thus using transfer learning, improves the efficiency of the algorithm when the data set is large. To determine the frequency band of each dispersive mode (effective frequency band) in the picked curves, the CNN outputs are post-processed by using measurements such as long/short moving average ratios of squared picked wavenumbers, posterior uncertainty of picked wavenumbers and wavenumbers in the picked curves or the power attribute. We demonstrate the effectiveness of this automatic picking by applying it to a 2-D line and a 3-D subset from a field ocean bottom node data set, where the fundamental and first higher modes of Scholte waves are accurately picked.

Influence of shear velocity on elastic full-waveform inversion: Gulf of Mexico case study using multicomponent ocean-bottom node data

Elastic full-waveform inversion (FWI) is superior to acoustic FWI due to its ability to simulate complex mode conversions in fast-varying elastic media. Using elastic FWI may become important when building velocity models in areas of large complex salt bodies that we typically see in the Gulf of Mexico. Accounting for elastic effects in FWI can reduce the artifacts that are caused by using an acoustic approximation. We often rely on petrophysics relations to define an initial shear velocity model, which also could be challenging. We demonstrate how to take advantage of the reflected converted waves from the top of the salt interface recorded in horizontal geophones to improve the estimation of the background shear velocity. Then, we illustrate the impact of the initial background shear velocity in the elastic inversion of the long-offset low-frequency hydrophone data. The workflow is based on a combination of wave-equation traveltime inversion, to cope with the cycle skipping, and FWI, to obtain higher-resolution results.

Separating Scholte Wave and Body Wave in OBN Data Using Wave-Equation Migration

The ocean bottom nodes (OBNs) acquire seismic data at a challenging depth to explore the subsurface structures. The recorded body wave and Scholte wave are highly mixed and are difficult to be separated. The strong body wave would influence the high-order modes extraction using the Scholte wave, while the Scholte wave would degrade the imaging of sedimentary structures using body wave. The lacking of effective methods for separating both waves prevents their application. We developed a migration-based method to accurately separate the Scholte wave and body wave in the OBN data. First, we use high-pass filtering to divide the original OBN data into three parts: background noise, high-frequency body wave, and the mixture of Scholte wave and low-frequency body wave. Then, we separate the Scholte wave and low-frequency body wave using migration and demigration based on the fact that they have different limits of reversible-migration velocity. Finally, we generate the separated body wave by subtracting the Scholte wave from the denoised OBN data. For the off-line data, the local orthogonalization method is required to retrieve the weak leakage of Scholte wave around the apices. Theoretical analyses and numerical experiments show that the proposed method can accurately separate Scholte wave and body wave without any visible artifacts while retaining most of their inherent properties. The separated body wave provides a high-quality input for imaging sedimentary structures, and the separated Scholte wave enables the extraction of high-order modes of dispersion curve that are crucial for high-resolution surface-wave inversion.

Fast Least-Squares Reverse Time Migration of OBN Down-Going Multiples

Nowadays the ocean bottom node (OBN) acquisition is widely used in oil and gas resource exploration and seismic monitoring. Conventional imaging algorithms of OBN data mainly focus on the processing of up-going primaries and down-going first-order multiples. Up-going multiples and higher-order down-going multiples are generally regarded as noise and should be eliminated or ignored in conventional migration methods. However, multiples carry abundant information about subsurface structures where primaries cannot achieve. To take full advantage of multiples, we propose a migration method using OBN down-going all-order multiples. And then the least-squares optimization algorithm is used to suppress crosstalks. Finally, a phase-encoding-based migration algorithm is developed to cut down the computational cost by blending several common receiver gathers together using random time delays and polarity reversals. Numerical experiments on the complex Marmousi model illustrate that the developed approach can enlarge the imaging area evidently, reduce the computational cost effectively, and enhance the image quality by suppressing crosstalks and improving the resolution.

Nonrepeatability effects on time-lapse 4D seismic full-waveform inversion for ocean-bottom node data

Full-waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. However, nonrepeatability (NR) issues can contaminate the data and cause artifacts in the estimation of 4D rock and fluid property changes. Therefore, evaluating and studying the NR effects on the 4D data and FWI results can help, for instance, discriminate inversion artifacts from true changes and guide seismic survey design and processing workflows. Using realistic reservoir models, data, and field measurements of NR, we find the effects of NR source-receiver position and seawater velocity changes on the data and the 4D FWI results. We show that ignoring these NR effects in the inversion can cause strong artifacts in the estimated velocity change models and thus should be addressed before or during inversion. The NR source-receiver positioning issue can be successfully addressed by 4D FWI, whereas the NR water velocity issue requires measurements or estimations of water velocities. Furthermore, we compare the accuracy and robustness of the parallel, double-difference, and central-difference 4D FWI methods to realistic NR ocean-bottom node data in a quantitative way. Parallel 4D FWI fails to capture geomechanical changes and also overestimates the aquifer layer changes with NR data. Double-difference 4D FWI is capable of recovering the geomechanical changes, but it is also sensitive to NR noises, generating more artifacts in the overburden. By averaging the forward and reverse bootstrap 4D estimates, central-difference 4D FWI is more robust to NR noises and also produces the most accurate 4D estimates.

Shenzi OBN: An imaging step change

The story of seismic imaging over BHP's Shenzi Gulf of Mexico production field follows the history of offshore seismic imaging, from 2D to 3D narrow-azimuth streamer acquisition and to its leading the wide-azimuth movement with the Shenzi rich-azimuth (RAZ) survey. Each RAZ reprocessing project over the last 15 years applied the latest processing technology, culminating in hundreds of scenario tests to refine the salt model, but eventually the RAZ data reached a technical limit. A new ocean-bottom-node (OBN) survey acquired in 2020 has produced a step-change improvement over the legacy RAZ image. The uplift can be attributed to several factors. First, an OBN feasibility and survey design study demonstrated that a core of dense nodes combined with sparse nodes would improve the accuracy and resolution of the full-waveform inversion (FWI) solution. Second, the OBN data acquired following the survey design and employing FWI as the main model-building tool realized the predicted improvement. The result was a substantial change to the complex salt model, verified by a salt proximity survey as well as other salt markers, and improvement in imaging over the entire field. In addition to the improvement arising from a more accurate FWI velocity model, the steep-dip imaging also benefited from the new full-azimuth and long-offset data. However, the best steep-dip and fault imaging comes from the FWI image, a direct estimation of reflectivity from the FWI velocity. As the maximum frequency used by FWI moves toward the maximum frequency of the final reverse time migration (RTM), the FWI image approaches the resolution necessary to compete as the primary interpretation volume. Its subsalt illumination surpassed that of the RTM and even the least-squares RTM volumes. These imaging improvements are providing a new understanding of the faults and stratigraphic relationships of the field.

First time-lapse OBN in Bonga deepwater offshore field

Time-lapse seismic data from Bonga Field, located in deepwater Nigeria, have delivered excellent results previously from dedicated streamer seismic surveys (2000, 2008, and 2012) that image changes in amplitude due to water replacing oil. One challenge with the streamer data is the presence of a floating production storage and offloading (FPSO) unit. It is difficult to accurately repeat streamer acquisition geometry in economically important updip regions of reservoirs that lie beneath the FPSO. To ensure an accurate repeat of this area, we acquired an ocean-bottom node (OBN) survey in 2010 and carried out the first time-lapse OBN repeat of the survey in 2018. Time-lapse processing of the OBN data produced excellent results. We obtained an OBN-on-OBN normalized root mean square (NRMS) difference repeatability of 6%. This was an improvement over the 12% NRMS for streamer-on-streamer repeats. The OBN data quality was unaffected by the FPSO, enabling us to properly image the changes occurring in this area. The 4D OBN interpretation was used to identify bypassed oil opportunities. The data also were used to derisk well placement, optimize water injection, and update the dynamic reservoir models. The new models are essential to enhance predictability and field performance. We also analyzed an area northwest of the field where we extended the 2018 OBN acquisition and compared it to streamer data to optimize a new injection well location.

Common reflection point stacking for ocean-bottom node data

Due to the asymmetrical ray paths caused by the large elevation differences between source and receiver locations in ocean-bottom node (OBN) survey, conventional common midpoint (CMP) stacking is unsuitable for processing OBN data. To mitigate this problem, we propose a common reflection point (CRP) stacking method, which enables us to efficiently produce accurate CRP-stacked image and stacking velocity model for subsequent processing workflow. For each input trace, we estimate the samples representing the reflection points in the output time section using stacking velocity, and then use a modified double-square-root (DSR) formula to map the amplitudes of input trace to these samples, which are progressively accumulated to form moveout-corrected CRP gathers. We can save computational costs by restricting the samples that need to be processed based on OBN reflection geometry, and use conventional moveout analysis of the CRP gathers to iteratively update the stacking velocity model. With tests on synthetic and field-data examples, we demonstrate the advantages of the proposed CRP stacking over conventional CMP stacking.

A study of full waveform inversion of seismic velocity structure under a rugged seafloor by using joint OBN and VSP synthetic seismic data

A numerical study of full waveform inversion (FWI) of seismic velocity structure was carried out by using synthetic ocean-bottom node (OBN) and vertical seismic profile (VSP) data. Based on an undulating seafloor modified from the commonly known Marmousi2 model, seismic forward modeling was performed to synthesize seismic P-wave data for the study. FWI experiments were performed using synthetic VSP, OBN and combined VSP + OBN data, separately, for the inversion of the velocity structure. The results show that the joint inversion of using the combined VSP + OBN synthetic data resulted in a very good velocity structure. Its accuracy is significantly higher than either using the VSP or OBN data alone. This suggests that the joint FWI using combined VSP + OBN data is a better image processing technique for the rugged seabed and complex structure below.

3D acoustic least-squares reverse time migration using the energy norm

We have developed a least-squares reverse time migration (LSRTM) method that uses an energy-based imaging condition to obtain faster convergence rates when compared with similar methods based on conventional imaging conditions. To achieve our goal, we also define a linearized modeling operator that is the proper adjoint of the energy migration operator. Our modeling and migration operators use spatial and temporal derivatives that attenuate imaging artifacts and deliver a better representation of the reflectivity and scattered wavefields. We applied the method to two Gulf of Mexico field data sets: a 2D towed-streamer benchmark data set and a 3D ocean-bottom node data set. We found LSRTM resolution improvement relative to RTM images, as well as the superior convergence rate obtained by the linearized modeling and migration operators based on the energy norm, coupled with inversion preconditioning using image-domain nonstationary matching filters.

The influence of volcanic rocks on the characterization of Rosebank Field – new insights from ocean-bottom seismic data and geological analogues integrated through interpretation and modelling

Abstract During Late Paleocene–Early Eocene times, the modern Rosebank structure was located at the juxtaposition of the easterly advancing Flett volcanic system and the northerly prograding Flett delta. As a result, the Rosebank reservoir sandstones are interstratified with volcanic and volcaniclastic rocks, offering challenges for reservoir imaging, depth prediction and reservoir characterization. These challenges have driven the application of Ocean Bottom Node (OBN) seismic technology. OBN data have yielded improved velocity models for depth conversion, better reservoir definition and key insights to aid the modelling of sand distribution from seismic attributes. Spectral decomposition of the OBN seismic data has facilitated the extraction of distinct volcanic subunits, whilst spectral enhancement has enabled visualization of complex stacking patterns within individual igneous layers. To complement the seismic analysis, detailed geological analogue studies have been undertaken in volcanic provinces such as the Palaeogene volcanic district of SE Greenland and the Columbia River Flood Basalt Province, USA. No single outcrop provides a definitive analogy to Rosebank, but each offers insights that provide an important link to understanding and managing the main subsurface uncertainties associated with field development. Integration of these multiple workflows have improved the reservoir characterization and provided the foundation for the optimization of the field development plan.

Finely sampled 3D angle gathers from sparse OBN data

Large-scale deepwater ocean-bottom node (OBN) surveys typically use a node spacing of about 400 m. We make 3D angle gathers from OBN data by mapping image traces to the reflection azimuth and reflection angle domain. Large node spacing may coarsely sample the seismic data in these angles. This may result in degraded and noisy angle gathers unless we properly account for the sampling during processing. We describe a method for making high-quality 3D angle gathers on finely sampled angle grids. We do this by properly band limiting the mapping operator with sinc functions defined in the angle domain. This method allows us to oversample the 3D angle gathers accurately in the angle domain and obtain high-quality results. We demonstrate our method with deepwater Gulf of Mexico node data and show that we can produce high-quality gathers even with 800 m node spacing. The angle gathers derived from these wider node data preserve residual moveout and therefore are suitable for model building. Our results add confidence that data acquired with nodes spaced farther apart than 400 m can be processed and imaged successfully.