Ocean Bottom Node Research Articles

Solving Mad Dog subsalt imaging in two decades: From WATS to OBN to elastic FWI

The Gulf of Mexico (GoM) is one of the most prolific oil and gas producing provinces in the world. The Mad Dog Field, like many large deepwater fields in the GoM, is subsalt. The geometric complexity of the overlying salt causes extremely variable image quality of the strata beneath the salt. Improving the seismic image has been critical for field development, and a tremendous amount of effort has been expended over the years to solve this problem. Over the past two decades, data acquisition has evolved from narrow-azimuth towed streamer to wide-azimuth streamer, and finally to ocean-bottom nodes. Processing methods such as using different anisotropic velocity models of increasing complexity, exhaustive iterations of salt modeling, acoustic full-waveform inversion, and most recently elastic full-waveform inversion have been applied. Dozens of wells have been drilled at Mad Dog guided by the resulting seismic images, and many acquisition and processing learnings have been acquired and implemented over this period to optimize the imaging. This paper explores the techniques that have caused major uplift to subsalt imaging and some techniques that were of only minor impact, while giving a glimpse into the imaging history of one of the GoM's giant fields.

New Concept Ocean-Bottom Multiphysics (OBMP) Nodes for Reservoir Monitoring

Marine-controlled source electromagnetics (CSEM) have been extensively applied to various exploration scenarios worldwide. However, its perceived value and cost relative to seismic and the scarcity of realistic case studies have limited the industry’s interest in time-lapse reservoir-monitoring (4D) applications. A feasible way to make demand for CSEM for 4D-monitoring programs would be to increase the value of information and reduce survey costs by performing joint operations where seismic and CSEM data are acquired during the same survey and at equivalent spatial densities. To this end, we propose a new multiphysics ocean-bottom nodes (OBN) concept and show the industry that CSEM can be a cost efficient and effective integrators to 4D seismic projects. To this end, we conducted a feasibility study demonstrating that horizontal magnetic field components have the required sensitivities and can be used instead of horizontal electric field components in mapping the 3D resistivity distribution and 4D fluid change responses in a given reservoir. This makes engineering a new OBN class simpler and cheaper, as various miniaturized magnetic field sensors are available off-the-shelf or readily working along with packaging and coupling solutions.

Target-oriented elastic full-waveform inversion through acoustic extended image-space redatuming

Elastic full-waveform inversion (FWI) can provide accurate and high-resolution subsurface parameters. However, its high computational cost prevents the application of this method to large-scale field-data scenarios. To mitigate this limitation, we have developed a target-oriented elastic FWI methodology based on a redatuming step that relies upon an extended least-squares migration process. In our approach, the surface-reflection data can be attributed to a given subsurface portion when mapped into the image space. This process allows us to reconstruct reflection data generated by a target area and recorded with a virtual acquisition geometry positioned directly above it. The redatuming step enables the application of an elastic FWI method within the target portion only. The entire workflow drastically decreases the overall cost of the surface-data inversion and allows the retrieval of accurate elastic parameters of the area of interest. We determine the effectiveness of our approach on a synthetic case based on the well-known Marmousi2 model and on 3D ocean-bottom node pressure data recorded in the Gulf of Mexico in which we retrieve the elastic parameters of a potential prospect, positioned in proximity of a salt-dome flank, and whose rock-physical properties are consistent with the presence of a gas-bearing sand reservoir.

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.

Deep learning for noise attenuation from the ocean bottom node 4C data

AbstractThe source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections.

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.

Average QP and QS estimation in marine sediments using a dense receiver array

A new spectral ratio method has been used to compute average P- and S-wave quality factors, [Formula: see text] and [Formula: see text], for the sedimentary sequence below the “V”-shaped Oseberg permanent reservoir monitoring (PRM) system. Quality factors are important for a more accurate characterization of the subsurface and to obtain additional information on the physical processes within the earth, such as fluid content and partial melt. However, few methods compute the average Q-value in sediments for P and S waves in a region, which can be used as a constraint for attenuation models in more sophisticated inversion techniques such as full-waveform inversion or attenuation tomography. To address this, a spectral ratio method using two receiving stations, one installed on sediment and one installed on bedrock, has been developed. This has resulted in quality factor estimations ranging from approximately 64 to 137 for P waves and 29 to 123 for S waves using a subset of the 172 ocean-bottom nodes in the Oseberg PRM system and a seismometer in Bergen, Norway. The [Formula: see text] values are more scattered and hence more uncertain than the [Formula: see text] values due to the P-wave signal being close to the background noise level. In the study area, local earthquakes of magnitude as low as [Formula: see text] 2.4 can be used to obtain [Formula: see text] values and earthquakes of magnitude lower than 2.1 to find [Formula: see text] values, with the possibility of using lower magnitude events if the epicentral distances to the receivers are similar and smaller. Using this method, the average quality factor in sediment packages, for a variety of regions, can be estimated using appropriate raypath configurations connecting source and receiver.

Elastic Seismic Imaging Enhancement of Sparse 4C Ocean-Bottom Node Data Using Deep Learning

The ocean bottom node (OBN) seismic acquisition system is designed to gather high-fidelity, wide-azimuth, and long-offset four-component (4C) data, which includes shear waves and enables the use of the elastic assumption in imaging and inversion. However, deploying geophysical instruments on the seafloor is difficult and costly, leading to the usual adoption of sparse node spacing. This can, however, lead to poor illumination and imaging challenges, especially in the shallow subsurface near the seafloor. To address these issues in the context of 4C elastic imaging, we propose a deep learning-based method using a multi-scale convolutional neural network (Ms-CNN) to improve the imaging quality of OBN surveys with sparse data acquisition. As an alternative to interpolating the sparse seismic data in the data domain, which can be a challenging task due to the limitations attributed to sampling theorem and the often larger amounts of data compared to the image, we train an Ms-CNN in a supervised fashion to map from sparse data images of PP and PS sections produced by 4C Gaussian beam migration to the equivalent dense data images, allowing for the direct processing of sparse data to improve imaging quality. Here, we combine the mean absolute error and multiscale structure similarity index measure in the loss function to optimize the network’s training process, and to help improve the performance. The effectiveness of the method is demonstrated through experiments on synthetic and field data, resulting in improved event continuity and reduced noise in migration results from sparse OBN acquisitions.

Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design

Seismic acquisition costs are directly associated with the number of sensors used in the survey. Limiting the number of sensors in a seismic survey can be beneficial, especially when sensors are expensive to purchase, deploy, and maintain. This work explores an optimal design method for ocean bottom node (OBN) detector deployment. The proposed method is based on a reinforcement learning approach. We assume access to an initial dataset over the area of study. These data are used to extract an over-complete pre-learned basis library via the proper orthogonal decomposition method, which leads to a fast least-squares seismic data reconstruction algorithm. Then, the sensor selection procedure entails using Q-learning to find the sensor configuration that maximizes the reconstruction quality.

Inversion-based multistage seismic data processing with physics-driven priors

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.

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.

Low-frequency ambient ocean-bottom node surface-wave seismology: A Gulf of Mexico case history

Long-time marine seismic recordings are becoming more common with the increased use of ocean-bottom nodes (OBNs), which can measure ambient seismic energy at frequencies lower than the typical minimum values in active-source compressed air-gun surveys. Interferometric processing on long-time ambient multicomponent data allows for the extraction of low-frequency (sub-2.0 Hz) responses in virtual source gathers (VSGs). Using 40 days of continuous OBN recordings acquired on a large dense array during a field experiment in the Gulf of Mexico, we find that sub-2.0 Hz surface-wave energy in the computed VSGs is strongly coherent and exhibits an identifiable spatially varying character. In particular, after rotating the data components from a Cartesian geographic into a polar wave-vector reference frame, we find that radial VSGs (i.e., oriented along the vector connecting the virtual source and receiver) clearly indicate that surface-wave propagation is influenced by salt bodies as identified in a colocated active-source survey situated at a minimum of 0.7 km depth below the seafloor, an observation consistent with calculated 0.25–0.50 Hz surface-wave sensitivity kernels. This suggests that low-frequency ambient OBN surface-wave seismology could be important for estimating the long-wavelength elastic material properties (particularly S-wave velocity) and identifying the lateral boundaries of salt bodies without any prior knowledge of subsurface geology.

Illumination guided sparse geometry optimization for target-oriented full-waveform inversion: An ocean bottom node synthetic study

For full-waveform inversion (FWI) with sparse survey layout, it is crucial to optimize the source and receiver geometry toward full-wave illumination. We propose a sparse geometry optimization method and workflow for target-oriented FWI using finite-frequency illumination analysis. The frequency-dependent impact of a source-receiver pair to the wavefield sensitivity kernel and diagonal terms of approximate Hessian matrix are derived under visco-acoustic model assumptions. Based on the reciprocity principle, we use virtual sources in target zone to efficiently calculate the Green's function and illumination from acquisition surface. Optimal locations of sparse receivers are determined by comparing band-limited illumination at reference stations and their surrounding region through an illumination attribute scan. FWI model residual errors are used to evaluate the performance of optimized geometry layout for a given target zone. The performances of different geometries are compared to determine a suitable frequency band for FWI geometry optimization. Synthetic deep water model and ocean bottom node acquisition geometry are used to verify the feasibility of the workflow. Numerical tests in target-oriented FWI show that optimized geometry with constraints of band-limited illumination below10 Hz has better performance than that above 10 Hz. An extension of our geometry optimization method to sparse source configuration is straightforward.

3‐D S Wave Imaging via Robust Neural Network Interpolation of 2‐D Profiles From Wave‐Equation Dispersion Inversion of Seismic Ambient Noise

AbstractAmbient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two‐step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross‐correlation of ambient noise signals. However, the two‐step methods have a 1‐D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2‐D wave‐equation dispersion (WD) inversion method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave‐equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data, which makes WD method less prone to getting stuck to local minima. In our study, the observed Scholte waves are generated by cross‐correlating continuous ambient noise signals recorded by each ocean bottom node (OBN) in the 3‐D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2‐D S velocity structure beneath the first line. We then use a robust neural network (NN)‐based method to interpolate the inverted 2‐D velocity slices to a continuous 3‐D velocity model and also generate a corresponding uncertainty model. Moreover, we compared the predicted dispersion curves and waveforms to the observed data, and a robust waveform and dispersion match are observed across all of the Gorgon OBN lines.

Simulating Large-Scale Streamer and OBN Acquisition over Subsalt Targets — an Example of Successful Remote Collaboration Between Survey Design and Survey Evaluation

In this article we look at how ray-based wavefront modelling can be used to evaluate competing acquisition techniques with significantly different illumination properties and associated acquisition cost. We compare subsurface illumination of complex sub-salt reservoirs typical of those found in the Gulf of Mexico for a variety of towed-streamer and full azimuth long offset ocean bottom node surveys. Single, dual and triple azimuth towed-streamer surveys are considered. This project was accomplished using a new connector between ACTeQ’s survey design and optimization software and Norsar’s 3D modelling software. This project is an excellent example of competitors collaborating to better achieve a customer’s needs. The project was also accomplished in a fully remote environment, and none of the authors were in the same city at any time during the project. Remote collaboration is becoming an increasingly effective low-carbon approach to complex challenges.

Full waveform inversion of a Brazilian pre-salt OBN dataset using a concentric circle source geometry

We have designed a workflow to apply full-waveform inversion (FWI) to a new type of acquisition recorded on ocean-bottom nodes (OBNs) in the Brazilian presalt area. The data set consists of three large-radius concentric circles of seismic source points recorded on OBN placed within the area of the circles. This geometry provides full azimuth distribution and benefits from long offsets in which the effect of diving waves is strong. These diving waves carry information about the presalt targets of interest. In the FWI workflow, we retrieve the P-wave velocity using the density as a propagation parameter. We test three objective functions: ℓ2-norm, ℓ1-norm, and nonparametric residuals objective function (NPR-FWI), which have different abilities to handle low signal-to-noise ratio data. In our inversion tests, the ℓ1-norm and NPR-FWI retrieve more information about the presalt reservoir than the ℓ1-norm. We find through several inversion experiments that we can recover structures related to the presalt reservoir, demonstrating the potential for this new type of OBN circular source geometry. We also discuss implications for low-cost data acquisition that take advantage of the sparse nature of sources and receivers needed in this survey geometry, including its extension to time-lapse target-oriented reservoir monitoring.

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.

Pattern-Review Technique Maximizes Hydrocarbon Recovery in Complex Brownfield

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203100, “Maximizing Hydrocarbon Recovery and Water-Injection Support Through Pattern Reviews Between Injector/Producer Pairs in a Large, Complex, and Faulted Brownfield in Southeast Asia,” by Arfan Ali, Manish K. Gupta, and Reza Salleh, Brunei Shell, et al. The paper has not been peer reviewed. _ Large, complex, faulted structures often pose substantial challenges in maximizing hydrocarbon recovery. The work presented in this paper has used what the authors call a “pattern-review” technique in a brownfield containing more than 500 well penetrations in southeast Asia. For the first time in field history, pattern reviews have been used extensively as a tool to understand reservoir connectivity and dynamic fluid movements across the field. An integrated effort involving different data sources and disciplines has provided fresh insights into the reservoirs that would have otherwise been difficult in heterolithic stacked reservoirs. Introduction The Champion field is 40 km offshore Brunei Darussalam in water depths of approximately 40 m. The field has a long, complex development history spanning almost 50 years. The field contains more than 45 platforms and well jackets. There are approximately 700 conduits in approximately 450 wellbores. For simplification, the field history has been divided into three main phases: - Phase I: Period of rapid expansion (focus on development of the Champion field main area) - Phase II: Period of consolidation (focus on waterflood initiation and development away from the main field area) - Phase III: Period of rejuvenation (focus on expansion in waterflood development) Because of the field’s complexity and unmappable flank areas, Phase I progressed outward from the center of the field. Appraisal wells from the existing platforms and other, isolated locations were targeted into unappraised blocks. This method of appraising and developing from known to less-known areas worked well in the initial development of the field. Phase II saw an increased emphasis on drilling water injectors in addition to infill oil producers. The lack of water injection and the resulting depletion in the main producing reservoir sands escalated the need for a Phase III development of the field. This phase heavily emphasized ramping up water injection in the field with the aim of forming a line-drive waterflood pattern between injector/producer pairs. The primary structure in the Champion field is a large anticline feature that is interpreted as a rollover into a regional west-heading growth fault system. Fault throws and transmissibilities vary across the entire field, with faults sealing increasingly from shallower to deeper reservoirs. Multiple sets of 3D seismic data have been acquired over the history of the field. In early 2020, a seismic ocean-bottom node survey was acquired over the field. In addition to the main bounding faults, several small-scale faults present randomly within the individual fault blocks. Ant-track maps have been used along with the seismic fault-structure map to understand these barriers. The reservoirs in the field consist of Miocene shallow marine, coastal, and deltaic sediments. The shallow reservoirs are heterolithic in nature. Not many wells have targeted these shallower reservoir intervals. A considerable amount of uncertainty surrounds the oil/water contact (OWC) in the field. As a result, a significant proportion of standard stock-tank oil initially in place remains in the ground.

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.