scholarly journals Low-frequency seismic: the next revolution in resolution

2014 ◽  
Vol 54 (1) ◽  
pp. 69
Author(s):  
Andrew Long ◽  
Cyrille Reiser
Keyword(s):  
New Technologies ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Well Control ◽  
Velocity Models ◽  
The Earth ◽  
Air Gun ◽  
Band Limited

Ultra-low seismic frequencies less than about 7 Hz cannot be produced by conventional air gun arrays, for any configuration and for any towing depth. There is a profound difference between improving low-frequency recovery by removing source and receiver ghosts (achievable) and improving low-frequency injection on the source side (an unrealised dream). If 1–7 Hz amplitudes could be usefully injected into the earth, it would be possible to facilitate much sharper seismic representation of geological contacts and internal features, and seismic inversion would yield robust and precise predictions of reservoir properties—without well control. The net result is fewer exploration and appraisal wells, greatly reduced exploration and development risks, and optimised recoverable reserves. Furthermore, an emerging seismic pursuit known as full waveform inversion (FWI) makes the bold promise that raw seismic field gathers can be directly used to invert for the highest achievable velocity models, almost without any human intervention. These models will bypass the traditional lack of low-frequency information in band-limited seismic data, and facilitate the aforementioned ambition of seismic inversion without well control. FWI, however, is confronted by the paradox that ultra-low-frequency seismic gathers are the necessary input for stable results. This paper describes new technologies that may enable the injection of strong 2–7 Hz amplitudes into the earth, and explains in simple terms how FWI can already be pursued as a robust complement to the prediction of accurate reservoir properties. The low-frequency revolution is already here.

Data-driven Prestack Waveform Inversion Using Genetic Algorithm- Methodology and Examples

2021 ◽  
pp. 1-97
Author(s):  
Lingxiao Jia ◽  
Subhashis Mallick ◽  
Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
P Wave ◽  
Data Driven ◽  
Initial Model ◽  
Reservoir Properties ◽  
Well Control ◽  
Seismic Waveform

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model, that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizonal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudo wells by running a two-pass one-dimensional isotropic elastic prestack waveform inversion that uses the reflectivity method for forward modeling and genetic algorithm for optimization. We then use the estimated pseudo wells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geological settings, we demonstrate the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is however necessary to improve the compute-efficiency of the methodology.

scholarly journals Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R1-R10 ◽  
Author(s):  
Zhendong Zhang ◽  
Tariq Alkhalifah ◽  
Zedong Wu ◽  
Yike Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Field Data ◽  
Extreme Values ◽  
Time Lag ◽  
Waveform Inversion ◽  
Optimal Time ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform ◽  
The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

Laplace-domain full-waveform inversion of seismic data lacking low-frequency information

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. R199-R206 ◽  
Author(s):  
Wansoo Ha ◽  
Changsoo Shin
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Single Frequency ◽  
Frequency Content ◽  
Laplace Domain ◽  
Frequency Information ◽  
Velocity Models ◽  
Gaussian Source

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.

scholarly journals Low-frequency acoustic signal created by rising air-gun bubble

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. P119-P128
Author(s):  
Daniel Wehner ◽  
Martin Landrø
Keyword(s):  
Acoustic Signal ◽  
Field Experiments ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Frequency Signal ◽  
Low Frequencies ◽  
Air Bubble ◽  
Rising Bubble ◽  
Air Gun ◽  
Different Depths

In the seismic industry, there is increasing interest in generating and recording low frequencies, which leads to better data quality and can be important for full-waveform inversion. The air gun is a seismic source with a signal that consists of the (1) main impulse, (2) oscillating bubble, and (3) rising of this air bubble. However, there has been little investigation of the third characteristic. We have studied a low-frequency signal that could be created by the rising air bubble and find the contribution to the low-frequency content in seismic acquisition. We use a simple theory and modeling of rising spheres in water and compute the acoustic signal created by this effect. We conduct tank and field experiments with a submerged buoy that is released from different depths and record the acoustic signal with hydrophones along the rising path. The experiments simulate the signal from the rising bubble separated from the other two effects (1 and 2). Furthermore, we use data recorded below a single air gun fired at different depths to investigate if we can observe the proposed signal. We find that the rising bubble creates a low-frequency signal. Compared with the main impulse and the oscillating bubble effect of an air-gun signal, the contribution of the rising bubble is weak, on the order of 1/900 depending on the bubble size. By using large air-gun arrays tuned to create one big bubble, the contribution of the signal can be increased. The enhanced signal can be important for deep targets or basin exploration because the low-frequency signal is less attenuated.

Seismic inversion of a carbonate buildup: A case study

2017 ◽  
Vol 5 (4) ◽  
pp. T641-T652 ◽  
Author(s):  
Mark Sams ◽  
Paul Begg ◽  
Timur Manapov
Keyword(s):  
Seismic Data ◽  
Probability Distributions ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Accurate Estimation ◽  
Amplitude Analysis ◽  
Simultaneous Inversion ◽  
Band Limited ◽  
Using Data

The information within seismic data is band limited and angle limited. Together with the particular physics and geology of carbonate rocks, this imposes limitations on how accurately we can predict the presence of hydrocarbons in carbonates, map the top carbonate, and characterize the porosity distribution through seismic amplitude analysis. Using data for a carbonate reef from the Nam Con Son Basin, Vietnam, the expectations based on rock-physics analysis are that the presence of gas can be predicted only when the porosity at the top of the carbonate is extremely high ([Formula: see text]), but that a fluid contact is unlikely to be observed in the background of significant porosity variations. Mapping the top of the carbonate (except when the top carbonate porosities are low) or a fluid contact requires accurate estimates of changes in [Formula: see text]. The seismic data do not independently support such an accurate estimation of sharp changes in [Formula: see text]. The standard approach of introducing low-frequency models and applying rock-physics constraints during a simultaneous inversion does not resolve the problems: The results are heavily biased by the well control and the initial interpretation of the top carbonate and fluid contact. A facies-based inversion in which the elastic properties are restricted to values consistent with the facies predicted to be present removes the well bias, but it does not completely obviate the need for a reasonably accurate initial interpretation in terms of prior facies probability distributions. Prestack inversion improves the quality of the facies predictions compared with a poststack inversion.

scholarly journals Extrapolated full-waveform inversion with deep learning

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R275-R288 ◽  
Author(s):  
Hongyu Sun ◽  
Laurent Demanet
Keyword(s):  
Neural Network ◽  
Numerical Experiments ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Bandwidth Extension ◽  
Low Frequencies ◽  
Full Waveform ◽  
The Neural Network ◽  
Band Limited

The lack of low-frequency information and a good initial model can seriously affect the success of full-waveform inversion (FWI), due to the inherent cycle skipping problem. Computational low-frequency extrapolation is in principle the most direct way to address this issue. By considering bandwidth extension as a regression problem in machine learning, we have adopted an architecture of convolutional neural network (CNN) to automatically extrapolate the missing low frequencies. The band-limited recordings are the inputs of the CNN, and, in our numerical experiments, a neural network trained from enough samples can predict a reasonable approximation to the seismograms in the unobserved low-frequency band, in phase and in amplitude. The numerical experiments considered are set up on simulated P-wave data. In extrapolated FWI (EFWI), the low-wavenumber components of the model are determined from the extrapolated low frequencies, before proceeding with a frequency sweep of the band-limited data. The introduced deep-learning method of low-frequency extrapolation shows adequate generalizability for the initialization step of EFWI. Numerical examples show that the neural network trained on several submodels of the Marmousi model is able to predict the low frequencies for the BP 2004 benchmark model. Additionally, the neural network can robustly process seismic data with uncertainties due to the existence of random noise, a poorly known source wavelet, and a different finite-difference scheme in the forward modeling operator. Finally, this approach is not subject to strong assumptions on signals or velocity models of other methods for bandwidth extension and seems to offer a tantalizing solution to the problem of properly initializing FWI.

Why don’t we measure seismic signatures?

Geophysics ◽  
1991 ◽  
Vol 56 (2) ◽  
pp. 190-201 ◽  
Author(s):  
A. Ziolkowski
Keyword(s):  
Impulse Response ◽  
Seismic Source ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Estimation Methods ◽  
Wavelet Estimation ◽  
The Earth ◽  
Band Limited ◽  
Marine Seismic ◽  
Lateral Variations

There are three related problems with our approach to signature deconvolution. First, there is a confusion among geophysicists about the basis of the convolutional model itself, which leads to doubts about the value of measurements of the source signature. Secondly, it is not generally recognized that statistical methods of wavelet estimation are unreliable. Thirdly, many explorationists are unaware that it is practical in many cases to make meaningful measurements of the source signature. The convolutional model of the reflection seismogram applies only for a point source, and is the convolution of the source signature with the impulse response of the earth, of Green’s function, which contains all possible arrivals, including reflections, refractions, multiples and diffractions. Stabilized deconvolution of the data with a known band‐limited signature is straightforward. The signature can be obtained by independent measurements, as described in the literature. The recovery of the elastic layer parameters from the band‐limited impulse response of the earth, after removal of the source signature by deconvolution, is the problem of inversion, and is not discussed in this paper. The theory of wave propagation does not support the commonly held view that a reflection seismogram can be regarded as a convolution of a wavelet with the series of normal‐incidence primary reflection coefficients. This is true of both prestack and poststack data. Poststack seismic inversion schemes, based on this model, that use well logs to extract the wavelet for predicting lateral variations in lithology away from the wells, rely on the wavelet to be laterally invariant. Even if there is perfect shot‐to‐shot repeatability, this model must yield a different wavelet at every well, and therefore the extracted wavelet does vary laterally. These schemes are therefore self‐contradictory and, in the worst cases, their results are likely to be worthless. Published methods for determining the source signature from measurements for the land vibrator, marine seismic source arrays, and dynamite on land are summarized. None of these methods appears to be in use. A Vibroseis example is included to show that the signal transmitted into the ground by the vibrators does not closely resemble the predetermined sweep, as is normally assumed. The transmitted signal could be determined in processing from measurements of the vibrator behaviour that are made in production for vibrator control, if only these measurements were recorded. Normally they are not. Instead of using measurements to determine the signature, the exploration industry relies on wavelet estimation methods that depend on both a model and statistical assumptions that have no theoretical justification.

Colored inversion

2017 ◽  
Vol 36 (10) ◽  
pp. 858-861 ◽  
Author(s):  
Martin Blouin ◽  
Erwan Gloaguen
Keyword(s):  
Physical Properties ◽  
Bulk Density ◽  
Acoustic Impedance ◽  
Seismic Velocity ◽  
Seismic Inversion ◽  
Fluid Content ◽  
The Earth ◽  
Band Limited ◽  
Reflectivity Data ◽  
Stochastic Seismic Inversion

Whether it is deterministic, band-limited, or stochastic, seismic inversion can bear many names depending on the algorithm used to produce it. Broadly, inversion converts reflectivity data to physical properties of the earth, such as acoustic impedance (AI), the product of seismic velocity and bulk density. This is crucial because, while reflectivity informs us about boundaries, impedance can be converted to useful earth properties such as porosity and fluid content via known petrophysical relationships.

Delaying the source ghost to improve the ultralow-frequency response of a marine air-gun source

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. P45-P51
Author(s):  
Honglei Shen ◽  
Thomas Elboth ◽  
Chunhui Tao ◽  
Gang Tian ◽  
Hanchuang Wang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Ultralow Frequency ◽  
Full Waveform ◽  
Air Gun ◽  
Marine Source ◽  
Frequency Output ◽  
Marine Seismic ◽  
Marine Air

The competing effect between the fundamental bubble and its source-ghost response results in a strong attenuation of the lowest frequencies (below 7 Hz). This loss cannot be compensated easily by adjusting the source depth. Consequently, the low-frequency content in marine seismic data is not optimal, degrading the performance of low-frequency dependent processing approaches, such as full-waveform inversion. To overcome this, we have developed an additional source to counteract the ghost from the main source. In this situation, the fundamental bubble is characterized by the depth of the main source, whereas the ghost response is characterized by the summed depth of the main and additional sources. This source setup mitigates the competing effect and reduces the suppression of ultralow frequencies. Compared with a conventional horizontal source, our source design will reduce the mid- to high-frequency output, which may be beneficial in situations in which environmental constraints limit the maximum allowed output of a marine source.

scholarly journals GEOLOGICAL CHARACTERIZATION OF EVAPORITIC SECTIONS AND ITS IMPACTS ON SEISMIC IMAGES: SANTOS BASIN, OFFSHORE BRAZIL

2019 ◽  
Vol 37 (1) ◽  
pp. 55
Author(s):  
Alexandre Rodrigo Maul ◽  
Marco Antonio Cetale Santos ◽  
Cleverson Guizan Silva ◽  
Josué Sá da Fonseca ◽  
María de Los Ángeles González Farias ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Seismic Imaging ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Velocity Models ◽  
Exploration And Production ◽  
Seismic Characterization ◽  
Well Data ◽  
Seismic Images

ABSTRACT. The pre-salt reservoirs in Santos Basin are known for being overlaid by thick evaporitic layers, which degrade the quality of seismic imaging and, hence, impacts reservoir studies. Better seismic characterization of this section can then improve decision making in E&P (Exploration and Production) projects. Seismic inversion - particularly with adequate low-frequency initial models – is currently the best approach to build good velocity models, leading to increased seismic resolution, more reliable amplitude response, and to attributes that can be quantitatively connected to well data. We discuss here a few considerations about inverting seismic data for the evaporitic section, and address procedures to improve reservoir characterization when using this methodology. The results show that we can obtain more realistic seismic images, better predicting both the reservoir positioning and its amplitude. Keywords: evaporitic section, seismic imaging, seismic inversion, reservoir characterization, seismic resolution.RESUMO. Os reservatórios do pré-sal da Bacia de Santos são conhecidos por estarem abaixo de uma espessa camada de evaporitos, que degradam a qualidade das imagens sísmicas e impactam os estudos de reservatórios. Melhores caracterizações desta seção podem, então, melhorar o processo de tomada de decisão em projetos de E&P (Exploração e Produção). Inversão sísmica – particularmente com modelos de baixa frequência inicial adequados – é correntemente a melhor abordagem para se construir modelos de velocidades, auxiliando no aumento de resolução sísmica, obtendo-se respostas de amplitude mais coerentes, e tendo seus atributos quantitativamente conectados com as informações de dados de poços. Aqui discutiremos algumas considerações sobre inversões sísmicas para seção evaporítica, e indicaremos procedimentos para melhorar a caracterização de reservatórios quando utilizando esta metodologia. Os resultados mostram que podemos obter imagens sísmicas mais realistas, com melhores predições tanto em termos de posicionamento quanto de sua amplitude.Palavras-chave: seção evaporítica, imagem sísmica, inversão sísmica, caracterização de reservatórios, resolução sísmica.

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