The domain of applicability of acoustic full-waveform inversion for marine seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC91-WCC103 ◽  
Author(s):  
Christophe Barnes ◽  
Marwan Charara
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Three Dimensions ◽  
Acoustic Medium ◽  
Physical Parameters ◽  
S Wave ◽  
Data Set ◽  
Reflection Seismic ◽  
Wave Inversion ◽  
Full Wave

Marine reflection seismic data inversion is a compute-intensive process, especially in three dimensions. Approximations often are made to limit the number of physical parameters we invert for, or to speed up the forward modeling. Because the data often are dominated by unconverted P-waves, one popular approximation is to consider the earth as purely acoustic, i.e., no shear modulus. The material density sometimes is taken as a constant. Nonlinear waveform seismic inversion consists of iteratively minimizing the misfit between the amplitudes of the measured and the modeled data. Approximations, such as assuming an acoustic medium, lead to incorrect modeling of the amplitudes of the seismic waves, especially with respect to amplitude variation with offset (AVO), and therefore have a direct impact on the inversion results. For evaluation purposes, we have performed a series of inversions with different approximations and different constraints whereby the synthetic data set to recover is computed for a 1D elastic medium. A series of numerical experiments, although simple, help to define the applicability domain of the acoustic assumption. Acoustic full-wave inversion is applicable only when the S-wave velocity and the density fields are smooth enough to reduce the AVO effect, or when the near-offset seismograms are inverted with a good starting model. However, in many realistic cases, acoustic approximation penalizes the full-wave inversion of marine reflection seismic data in retrieving the acoustic parameters.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Frequency‐domain acoustic‐wave modeling and inversion of crosshole data: Part II—Inversion method, synthetic experiments and real‐data results

Geophysics ◽  
1995 ◽  
Vol 60 (3) ◽  
pp. 796-809 ◽  
Author(s):  
Zhong‐Min Song ◽  
Paul R. Williamson ◽  
R. Gerhard Pratt
Keyword(s):  
Frequency Domain ◽  
Real Data ◽  
Forward Modeling ◽  
Two Dimensions ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Data Set ◽  
Wave Inversion ◽  
Full Wave ◽  
And Inversion

In full‐wave inversion of seismic data in complex media it is desirable to use finite differences or finite elements for the forward modeling, but such methods are still prohibitively expensive when implemented in 3-D. Full‐wave 2-D inversion schemes are of limited utility even in 2-D media because they do not model 3-D dynamics correctly. Many seismic experiments effectively assume that the geology varies in two dimensions only but generate 3-D (point source) wavefields; that is, they are “two‐and‐one‐half‐dimensional” (2.5-D), and this configuration can be exploited to model 3-D propagation efficiently in such media. We propose a frequency domain full‐wave inversion algorithm which uses a 2.5-D finite difference forward modeling method. The calculated seismogram can be compared directly with real data, which allows the inversion to be iterated. We use a descents‐related method to minimize a least‐squares measure of the wavefield mismatch at the receivers. The acute nonlinearity caused by phase‐wrapping, which corresponds to time‐domain cycle‐skipping, is avoided by the strategy of either starting the inversion using a low frequency component of the data or constructing a starting model using traveltime tomography. The inversion proceeds by stages at successively higher frequencies across the observed bandwidth. The frequency domain is particularly efficient for crosshole configurations and also allows easy incorporation of attenuation, via complex velocities, in both forward modeling and inversion. This also requires the introduction of complex source amplitudes into the inversion as additional unknowns. Synthetic studies show that the iterative scheme enables us to achieve the theoretical maximum resolution for the velocity reconstruction and that strongly attenuative zones can be recovered with reasonable accuracy. Preliminary results from the application of the method to a real data set are also encouraging.

Improving seismic QP estimation using rock-physics constraints

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. MR187-MR198 ◽  
Author(s):  
Yi Shen ◽  
Jack Dvorkin ◽  
Yunyue Li
Keyword(s):  
Seismic Data ◽  
Closed Form Solution ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Analytical Relation ◽  
Gas Reservoir ◽  
Seismic Section ◽  
Data Set ◽  
Attenuation Model ◽  
Text Model

Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.

Twelve years of vertical birefringence in nine‐component VSP data

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 582-597 ◽  
Author(s):  
Donald F. Winterstein ◽  
Gopa S. De ◽  
Mark A. Meadows
Keyword(s):  
Seismic Data ◽  
Azimuthal Anisotropy ◽  
Vertical Shear ◽  
S Wave ◽  
Seismic Profiles ◽  
Surface Reflection ◽  
Vertical Seismic Profiles ◽  
Reflection Seismic ◽  
San Andreas ◽  
Vsp Data

Since 1986, when industry scientists first publicly showed data supporting the presence of azimuthal anisotropy in sedimentary rock, we have studied vertical shear‐wave (S-wave) birefringence in 23 different wells in western North America. The data were from nine‐component vertical seismic profiles (VSPs) supplemented in recent years with data from wireline crossed‐dipole logs. This paper summarizes our results, including birefringence results in tabular form for 54 depth intervals in 19 of those 23 wells. In the Appendix we present our conclusions about how to record VSP data optimally for study of vertical birefringence. We arrived at four principal conclusions about vertical S-wave birefringence. First, birefringence was common but not universal. Second, birefringence ranged from 0–21%, but values larger than 4% occurred only in shallow formations (<1200 m) within 40 km of California’s San Andreas fault. Third, at large scales birefringence tended to be blocky. That is, both the birefringence magnitude and the S-wave polarization azimuth were often consistent over depth intervals of several tens to hundreds of meters but then changed abruptly, sometimes by large amounts. Birefringence in some instances diminished with depth and in others increased with depth, but in almost every case a layer near the surface was more birefringent than the layer immediately below it. Fourth, observed birefringence patterns generally do not encourage use of multicomponent surface reflection seismic data for finding fractured hydrocarbon reservoirs, but they do encourage use of crossed‐dipole logs to examine them. That is, most reservoirs were birefringent, but none we studied showed increased birefringence confined to the reservoir.

Accuracy Comparison of Elastic Impedance Formula

2012 ◽  
Vol 466-467 ◽  
pp. 400-404
Author(s):  
Jin Zhang ◽  
Huai Shan Liu ◽  
Si You Tong ◽  
Lin Fei Wang ◽  
Bing Xu
Keyword(s):  
Seismic Data ◽  
Poisson Ratio ◽  
Seismic Inversion ◽  
P Wave ◽  
S Wave ◽  
Elastic Parameters ◽  
Accuracy Comparison ◽  
Elastic Impedance ◽  
Prestack Seismic Inversion ◽  
Lamé Coefficients

Elastic impedance (EI) inversion is one of the prestack seismic inversion methods, which can obtain P-wave and S-wave velocity, density, Poisson ratio, Lame coefficients and other elastic parameters. But there have been many EI formulas nowadays, so which formula should be used in inversion is an urgent problem. This paper divides these formulas into two categories, and use several forward modeling to test the accuracy of these EI formulas. It shows that using the first kind of EI formulas in near offset seismic data can get high precision results.

scholarly journals Sensitivity Analysis of FWI Applied to OVSP Synthetic Data for Fault Detection and Characterization in Crystalline Rocks

Geosciences ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 442
Author(s):  
Yassine Abdelfettah ◽  
Christophe Barnes
Keyword(s):  
Fault Detection ◽  
Reference Model ◽  
Synthetic Data ◽  
Inversion Method ◽  
Physical Parameters ◽  
Wave Inversion ◽  
Full Wave ◽  
Imaging Results ◽  
Antenna Length ◽  
Sensitivity Studies

We have performed several sensitivity studies to assess the ability of the Full Wave Inversion method to detect, delineate and characterize faults in a crystalline geothermal reservoir from OVSP data. The distant goal is to apply the method to the Soultz-sous-Forêts site (France). Our approach consists of performing synthetic Full Wave 2D Inversion experiments using offset vertical seismic and comparing the estimated fields provided by the inversion, i.e., the estimated underground images, to the initial reference model including the fault target. We first tuned the inversion algorithmic parameters in order to adapt the FWI software, originally dedicated to a sedimentary context, to a crystalline context. In a second step, we studied the sensitivity of the FWI fault imaging results as a function of the acquisition geometry parameters, namely, the number of shots, the intershot distance, the maximum offset and also the antenna length and well deviation. From this study, we suggest rules to design the acquisition geometry in order to improve the fault detection, delineation and characterization. In a third step, we studied the sensitivity of the FWI fault imaging results as a function of the fault or the fault zone characteristics, namely, the fault dip, thickness and the contrast of physical parameters between the fault materials and the surrounding fresh rocks. We have shown that a fault with high dip, between 60 and 90° as thin as 10 m (i.e. lower than a tenth of the seismic wavelength of 120 m for Vp and 70 m for Vs) can be imaged by FWI, even in the presence of additive gaussian noise. In summary, for a crystalline geological context, and dealing with acceptable S/N ratio data, the FWI show a high potential for accurately detecting, delineating and characterizing the fault zones.

Reflectivity decomposition: Theory and application

2021 ◽  
pp. 1-57
Author(s):  
Chen Liang ◽  
John Castagna ◽  
Marcelo Benabentos
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reflection Coefficients ◽  
3D Seismic ◽  
Decomposition Theory ◽  
Midland Basin ◽  
Reflection Seismic ◽  
Sparse Inversion ◽  
Reflectivity Inversion

Sparse reflectivity inversion of processed reflection seismic data is intended to produce reflection coefficients that represent boundaries between geological layers. However, the objective function for sparse inversion is usually dominated by large reflection coefficients which may result in unstable inversion for weak events, especially those interfering with strong reflections. We propose that any seismogram can be decomposed according to the characteristics of the inverted reflection coefficients which can be sorted and subset by magnitude, sign, and sequence, and new seismic traces can be created from only reflection coefficients that pass sorting criteria. We call this process reflectivity decomposition. For example, original inverted reflection coefficients can be decomposed by magnitude, large ones removed, the remaining reflection coefficients reconvolved with the wavelet, and this residual reinverted, thereby stabilizing inversions for the remaining weak events. As compared with inverting an original seismic trace, subtle impedance variations occurring in the vicinity of nearby strong reflections can be better revealed and characterized when only the events caused by small reflection coefficients are passed and reinverted. When we apply reflectivity decomposition to a 3D seismic dataset in the Midland Basin, seismic inversion for weak events is stabilized such that previously obscured porous intervals in the original inversion, can be detected and mapped, with good correlation to actual well logs.

SH-SH wave inversion for S-wave velocity and density

Geophysics ◽  
2022 ◽  
pp. 1-59
Author(s):  
Fucai Dai ◽  
Feng Zhang ◽  
Xiangyang Li
Keyword(s):  
Wave Velocity ◽  
Inversion Method ◽  
Data Sets ◽  
Sh Wave ◽  
S Wave ◽  
Data Set ◽  
Sh Waves ◽  
Model Parameter ◽  
Wave Inversion ◽  
Impedance Contrast

SS-waves (SV-SV waves and SH-SH waves) are capable of inverting S-wave velocity ( VS) and density ( ρ) because they are sensitive to both parameters. SH-SH waves can be separated from multicomponent data sets more effectively than the SV-SV wave because the former is decoupled from the PP-wave in isotropic media. In addition, the SH-SH wave can be better modeled than the SV-SV wave in the case of strong velocity/impedance contrast because the SV-SV wave has multicritical angles, some of which can be quite small when velocity/ impedance contrast is strong. We derived an approximate equation of the SH-SH wave reflection coefficient as a function of VS and ρ in natural logarithm variables. The approximation has high accuracy, and it enables the inversion of VS and ρ in a direct manner. Both coefficients corresponding to VS and ρ are “model-parameter independent” and thus there is no need for prior estimate of any model parameter in inversion. Then, we developed an SH-SH wave inversion method, and demonstrated it by using synthetic data sets and a real SH-SH wave prestack data set from the west of China. We found that VS and ρ can be reliably estimated from the SH-SH wave of small angles.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Robust deep learning seismic inversion with a priori initial model constraint

2021 ◽  
Author(s):  
Jian Zhang ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Yuanqiang Li ◽  
Guangtan Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
A Priori ◽  
Seismic Inversion ◽  
Training Dataset ◽  
Initial Model ◽  
Data Set ◽  
The Real ◽  
Model Constraint ◽  
Trained Network

Summary Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing deep learning-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labeled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training dataset is firstly generated based on forward modeling. The network is then pre-trained using the synthetic training dataset, which is further validated using synthetic data that has not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labeled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2D data including both post-stack and pre-stack examples, as well as a real 3D post-stack seismic data set from the western Canadian sedimentary basin.

Sign in / Sign up

Export Citation Format

Share Document