Land FWI in the Delaware Basin, west Texas: A case study

2020 ◽  
Vol 39 (5) ◽  
pp. 324-331
Author(s):  
Gary Murphy ◽  
Vanessa Brown ◽  
Denes Vigh
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
High Signal ◽  
Data Sets ◽  
Data Set ◽  
Low Frequencies ◽  
Near Surface ◽  
Delaware Basin ◽  
West Texas

As part of a wide-reaching full-waveform inversion (FWI) research program, FWI is applied to an onshore seismic data set collected in the Delaware Basin, west Texas. FWI is routinely applied on typical marine data sets with high signal-to-noise ratio (S/N), relatively good low-frequency content, and reasonably long offsets. Land seismic data sets, in comparison, present significant challenges for FWI due to low S/N, a dearth of low frequencies, and limited offsets. Recent advancements in FWI overcome limitations due to poor S/N and low frequencies making land FWI feasible to use to update the shallow velocities. The chosen area has contrasting and variable near-surface conditions providing an excellent test data set on which to demonstrate the workflow and its challenges. An acoustic FWI workflow is used to update the near-surface velocity model in order to improve the deeper image and simultaneously help highlight potential shallow drilling hazards.

Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Velocity Mapping ◽  
Signal To Noise ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

scholarly journals Time-lapse difference static correction using prestack crosscorrelations: 4D seismic image enhancement case from Ketzin

Geophysics ◽  
2014 ◽  
Vol 79 (6) ◽  
pp. B243-B252 ◽  
Author(s):  
Peter Bergmann ◽  
Artem Kashubin ◽  
Monika Ivandic ◽  
Stefan Lüth ◽  
Christopher Juhlin
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Data Sets ◽  
Storage Site ◽  
Data Set ◽  
Near Surface ◽  
Static Correction ◽  
Seismic Image ◽  
Static Corrections ◽  
4D Seismic

A method for static correction of time-lapse differences in reflection arrival times of time-lapse prestack seismic data is presented. These arrival-time differences are typically caused by changes in the near-surface velocities between the acquisitions and had a detrimental impact on time-lapse seismic imaging. Trace-to-trace time shifts of the data sets from different vintages are determined by crosscorrelations. The time shifts are decomposed in a surface-consistent manner, which yields static corrections that tie the repeat data to the baseline data. Hence, this approach implies that new refraction static corrections for the repeat data sets are unnecessary. The approach is demonstrated on a 4D seismic data set from the Ketzin [Formula: see text] pilot storage site, Germany, and is compared with the result of an initial processing that was based on separate refraction static corrections. It is shown that the time-lapse difference static correction approach reduces 4D noise more effectively than separate refraction static corrections and is significantly less labor intensive.

Fully automated near-surface analysis by surface-consistent refraction method

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. U39-U49 ◽  
Author(s):  
Daniele Colombo ◽  
Federico Miorelli ◽  
Ernesto Sandoval ◽  
Kevin Erickson
Keyword(s):  
Surface Analysis ◽  
Seismic Data ◽  
Linear Equations ◽  
Time Shift ◽  
Data Sets ◽  
Time Lags ◽  
Data Set ◽  
Near Surface ◽  
Seismic Acquisition ◽  
Key Aspects

Industry practices for near-surface analysis indicate difficulties in coping with the increased number of channels in seismic acquisition systems, and new approaches are needed to fully exploit the resolution embedded in modern seismic data sets. To achieve this goal, we have developed a novel surface-consistent refraction analysis method for low-relief geology to automatically derive near-surface corrections for seismic data processing. The method uses concepts from surface-consistent analysis applied to refracted arrivals. The key aspects of the method consist of the use of common midpoint (CMP)-offset-azimuth binning, evaluation of mean traveltime and standard deviation for each bin, rejection of anomalous first-break (FB) picks, derivation of CMP-based traveltime-offset functions, conversion to velocity-depth functions, evaluation of long-wavelength statics, and calculation of surface-consistent residual statics through waveform crosscorrelation. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by crosscorrelating a pilot trace with all the other traces in the gather in which the correlation window is centered at the refracted arrival. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. All the steps are completely automated and require a fraction of the time needed for conventional near-surface analysis. The developed methodology was successfully performed on a complex 3D land data set from Central Saudi Arabia where it was benchmarked against a conventional tomographic work flow. The results indicate that the new surface-consistent refraction statics method enhances seismic imaging especially in portions of the survey dominated by noise.

scholarly journals Evolutionary full-waveform inversion

2020 ◽  
Vol 224 (1) ◽  
pp. 306-311
Author(s):  
Dirk Philip van Herwaarden ◽  
Michael Afanasiev ◽  
Solvi Thrastarson ◽  
Andreas Fichtner
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Data Set ◽  
New Approach ◽  
Full Waveform ◽  
Upper Mantle Structure ◽  
Data Coverage ◽  
D Model

SUMMARY We present a new approach to full-waveform inversion (FWI) that enables the assimilation of data sets that expand over time without the need to reinvert all data. This evolutionary inversion rests on a reinterpretation of stochastic Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS), which randomly exploits redundancies to achieve convergence without ever considering the data set as a whole. Specifically for seismological applications, we consider a dynamic mini-batch stochastic L-BFGS, where the size of mini-batches adapts to the number of sources needed to approximate the complete gradient. As an illustration we present an evolutionary FWI for upper-mantle structure beneath Africa. Starting from a 1-D model and data recorded until 1995, we sequentially add contemporary data into an ongoing inversion, showing how (i) new events can be added without compromising convergence, (ii) a consistent measure of misfit can be maintained and (iii) the model evolves over times as a function of data coverage. Though applied retrospectively in this example, our method constitutes a possible approach to the continuous assimilation of seismic data volumes that often tend to grow exponentially.

High-resolution Radon preconditioning for full-waveform inversion of land seismic data

2017 ◽  
Vol 5 (4) ◽  
pp. SR23-SR33 ◽  
Author(s):  
Xin Cheng ◽  
Kun Jiao ◽  
Dong Sun ◽  
Zhen Xu ◽  
Denes Vigh ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform

Over the past decade, acoustic full-waveform inversion (FWI) has become one of the standard methods in the industry to construct high-resolution velocity fields from the seismic data acquired. While most of the successful applications are for marine acquisition data with rich low-frequency diving or postcritical waves at large offsets, the application of acoustic FWI on land data remains a challenging topic. Land acoustic FWI application faces many severe difficulties, such as the presence of strong elastic effects, large near-surface velocity contrast, and heterogeneous, topography variations, etc. In addition, it is well-known that low-frequency transmitted seismic energy is crucial for the success of FWI to overcome sensitivity to starting velocity fields; unfortunately, those are the parts of the data that suffer the most from a low signal-to-noise ratio (S/N) in land acquisition. We have developed an acoustic FWI application on a land data set from North Kuwait, and demonstrated our solutions to mitigate some of the challenges posed by land data. More specifically, we have developed a semblance-based high-resolution Radon (HR-Radon) inversion approach to enhance the S/N of the low-frequency part of the FWI input data and to ultimately improve the convergence of the land FWI workflow. To mitigate the impact of elastic effects, we included only the diving and postcritical early arrivals in the waveform inversion. Our results show that, with the aid of HR-Radon preconditioning and a carefully designed workflow, acoustic FWI has the ability to derive a reliable high-resolution near-surface model that could not be otherwise recovered through traditional tomographic methods.

An application of full-waveform inversion to land data using the pseudo-Hessian matrix

2016 ◽  
Vol 4 (4) ◽  
pp. T627-T635
Author(s):  
Yikang Zheng ◽  
Wei Zhang ◽  
Yibo Wang ◽  
Qingfeng Xue ◽  
Xu Chang
Keyword(s):  
Hessian Matrix ◽  
Waveform Inversion ◽  
Computational Cost ◽  
Velocity Model ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Near Surface ◽  
Full Waveform ◽  
The Time Domain

Full-waveform inversion (FWI) is used to estimate the near-surface velocity field by minimizing the difference between synthetic and observed data iteratively. We apply this method to a data set collected on land. A multiscale strategy is used to overcome the local minima problem and the cycle-skipping phenomenon. Another obstacle in this application is the slow convergence rate. The inverse Hessian can enhance the poorly blurred gradient in FWI, but obtaining the full Hessian matrix needs intensive computation cost; thus, we have developed an efficient method aimed at the pseudo-Hessian in the time domain. The gradient in our FWI workflow is preconditioned with the obtained pseudo-Hessian and a synthetic example verifies its effectiveness in reducing computational cost. We then apply the workflow on the land data set, and the inverted velocity model is better resolved compared with traveltime tomography. The image and angle gathers we get from the inversion result indicate more detailed information of subsurface structures, which will contribute to the subsequent seismic interpretation.

scholarly journals Regularized full-waveform inversion with automated salt flooding

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R569-R582 ◽  
Author(s):  
Mahesh Kalita ◽  
Vladimir Kazei ◽  
Yunseok Choi ◽  
Tariq Alkhalifah
Keyword(s):  
Optimization Problem ◽  
Waveform Inversion ◽  
Dominant Frequency ◽  
Model Parameters ◽  
Constant Density ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Low Frequencies ◽  
Full Waveform

Full-waveform inversion (FWI) attempts to resolve an ill-posed nonlinear optimization problem to retrieve the unknown subsurface model parameters from seismic data. In general, FWI fails to obtain an adequate representation of models with large high-velocity structures over a wide region, such as salt bodies and the sediments beneath them, in the absence of low frequencies in the recorded seismic signal, due to nonlinearity and nonuniqueness. We alleviate the ill posedness of FWI associated with data sets affected by salt bodies using model regularization. We have split the optimization problem into two parts: First, we minimize the data misfit and the total variation in the model, seeking to achieve an inverted model with sharp interfaces; and second, we minimize sharp velocity drops with depth in the model. Unlike conventional industrial salt flooding, our technique requires minimal human intervention and no information about the top of the salt. Those features are demonstrated on data sets of the BP 2004 and Sigsbee2A models, synthesized from a Ricker wavelet of dominant frequency 5.5 Hz and minimum frequency 3 Hz. We initiate the inversion process with a simple model in which the velocity increases linearly with depth. The model is well-retrieved when the same constant density acoustic code is used to simulate the observed data, which is still one of the most common FWI tests. Moreover, our technique allows us to reconstruct a reasonable depiction of the salt structure from the data synthesized independently with the BP 2004 model with variable density. In the Sigsbee2A model, we manage to even capture some of the fine layering beneath the salt. In addition, we evaluate the versatility of our method on a field data set from the Gulf of Mexico.

Multiparameter waveform inversion of a large wide-azimuth low-frequency land data set in Oman

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. WA69-WA77 ◽  
Author(s):  
Alexandre Stopin ◽  
René-Édouard Plessix ◽  
Said Al Abri
Keyword(s):  
Waveform Inversion ◽  
Low Frequency ◽  
Large Data ◽  
Earth Model ◽  
Data Sets ◽  
Anisotropic Parameter ◽  
Data Set ◽  
Low Frequencies ◽  
Reflected Waves ◽  
Simultaneous Inversion

Several 3D seismic acoustic full-waveform inversions (FWIs) of offshore data sets have been reported over the last five years. A successful updating of the long-to-intermediate wavelengths of the earth model by FWI requires good-quality wide-angle, long-offset, low-frequency data. Recent improvements in acquisition make such data sets available on land, too. We evaluated a 3D application on a data set recorded in North Oman. The data contain low frequencies down to 1.5 Hz, long-offsets, and wide azimuths. The application of acoustic FWI on land remains complicated because of the elastic effects, notably the strong ground-roll and many acquisition and human-activity-related noises. The presence of fast carbonate layers in this region induces velocity inversions, difficult to recover from diving or postcritical waves. We accounted for anisotropic effects as we include FWI in a classical structural imaging workflow. With a dedicated processing of the data and a simultaneous inversion of the NMO velocity and the anelliptic-anisotropic parameter, we succeeded to interpret the kinematics of transmitted and reflected waves, although in the waveform inversion we included only the diving and postcritical waves. This approach has some limitations because of the acoustic assumption. We could not obtain a high-resolution image, especially at the shale-carbonate interfaces. There is also a trade-off between the NMO velocity and the anelliptic anisotropic parameter. However, the image improvements after acoustic FWI and the ability to handle the large data volume make this technique attractive in an imaging workflow.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

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