scholarly journals Joint interpretation of magnetotelluric, seismic and well-log data in Hontomín (Spain)

2016 ◽  
Author(s):  
X. Ogaya ◽  
J. Alcalde ◽  
I. Marzán ◽  
J. Ledo ◽  
P. Queralt ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Co2 Injection ◽  
Well Log ◽  
Storage Site ◽  
Near Surface ◽  
Log Data ◽  
Velocity Models ◽  
3D Velocity Model ◽  
Joint Interpretation

Abstract. Hontomín (N of Spain) hosts the first Spanish CO2 storage pilot plant. The subsurface characterisation of the site included the acquisition of a 3D seismic reflection and a circumscribed 3D magnetotelluric (MT) survey. This paper addresses the combination of the seismic and MT results, together with the available well-log data, in order to achieve a better characterisation of the Hontomín subsurface. We compare the structural model obtained from the interpretation of the seismic data with the geoelectrical model resulting from the MT data. The models corr elate well in the surroundings of the CO2 injection area with the major structural observed related to the presence of faults. The combination of the two methods allowed a more detailed characterisation of the faults, defining their structural and fluid flow characteristics, which is key for the risk assessment of the storage site. Moreover, we use the well-log data of the existing wells to derive resistivity-velocity relationships for the subsurface formations and compute a 3D velocity model of the site using the 3D resistivity model as a reference. The derived velocity model is compared to both the predicted and logged velocity in the injection and monitoring wells, for an overall assessment of the resistivity-velocity relationships computed. Finally, the derived velocity model is compared in the near surface with the velocity model used for the static corrections in the seismic data. The results allowed extracting information about the characteristics of the shallow subsurface, enhancing the presence of clays and water content variations. The good correlation of t he velocity models and well-log data demonstrate the potential of the two methods for characterising the subsurface, in terms of its physical properties (velocity, resistivity) and structural/reservoir characteristics. This work explores the compatibility of the seismic and magnetotelluric methods across scales highlighting the importance of joint interpretation in reservoir characterisation.

scholarly journals Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 943-958 ◽  
Author(s):  
Xènia Ogaya ◽  
Juan Alcalde ◽  
Ignacio Marzán ◽  
Juanjo Ledo ◽  
Pilar Queralt ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Unconsolidated Sediments ◽  
Well Log ◽  
Storage Site ◽  
Near Surface ◽  
Log Data ◽  
Velocity Models ◽  
Joint Interpretation

Abstract. Hontomín (N of Spain) hosts the first Spanish CO2 storage pilot plant. The subsurface characterization of the site included the acquisition of a 3-D seismic reflection and a circumscribed 3-D magnetotelluric (MT) survey. This paper addresses the combination of the seismic and MT results, together with the available well-log data, in order to achieve a better characterization of the Hontomín subsurface. We compare the structural model obtained from the interpretation of the seismic data with the geoelectrical model resulting from the MT data. The models correlate well in the surroundings of the CO2 injection area with the major structural differences observed related to the presence of faults. The combination of the two methods allowed a more detailed characterization of the faults, defining their geometry, and fluid flow characteristics, which are key for the risk assessment of the storage site. Moreover, we use the well-log data of the existing wells to derive resistivity–velocity relationships for the subsurface and compute a 3-D velocity model of the site using the 3-D resistivity model as a reference. The derived velocity model is compared to both the predicted and logged velocity in the injection and monitoring wells, for an overall assessment of the computed resistivity–velocity relationships. The major differences observed are explained by the different resolution of the compared geophysical methods. Finally, the derived velocity model for the near surface is compared with the velocity model used for the static corrections in the seismic data. The results allowed extracting information about the characteristics of the shallow unconsolidated sediments, suggesting possible clay and water content variations. The good correlation of the velocity models derived from the resistivity–velocity relationships and the well-log data demonstrate the potential of the combination of the two methods for characterizing the subsurface, in terms of its physical properties (velocity, resistivity) and structural/reservoir characteristics. This work explores the compatibility of the seismic and magnetotelluric methods across scales highlighting the importance of joint interpretation in near surface and reservoir characterization.

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

2021 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Computational Cost ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Inversion ◽  
Velocity Models ◽  
North East ◽  
3D Velocity Model ◽  
Zero Offset

<p>Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.</p>

Two robust imaging methodologies for challenging environments: Wave-equation dispersion inversion of surface waves and guided waves and supervirtual interferometry + tomography for far-offset refractions

2018 ◽  
Vol 6 (4) ◽  
pp. SM27-SM37 ◽  
Author(s):  
Jing Li ◽  
Kai Lu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Wave Equation ◽  
Velocity Distribution ◽  
Surface Waves ◽  
Guided Waves ◽  
Velocity Model ◽  
Dispersion Curves ◽  
P Wave ◽  
Head Wave ◽  
Near Surface ◽  
Velocity Models

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

scholarly journals Passive seismic event estimation using multiscattering waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. KS59-KS69 ◽  
Author(s):  
Chao Song ◽  
Zedong Wu ◽  
Tariq Alkhalifah
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Source Image ◽  
Time Inversion ◽  
Multiple Sources ◽  
Origin Time ◽  
Secondary Sources ◽  
Velocity Models ◽  
Passive Seismic

Passive seismic monitoring has become an effective method to understand underground processes. Time-reversal-based methods are often used to locate passive seismic events directly. However, these kinds of methods are strongly dependent on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used on passive seismic data to invert the velocity model and source image, simultaneously. However, waveform inversion of passive seismic data uses mainly the transmission energy, which results in poor illumination and low resolution. We developed a waveform inversion using multiscattered energy for passive seismic to extract more information from the data than conventional FWI. Using transmission wavepath information from single- and double-scattering, computed from a predicted scatterer field acting as secondary sources, our method provides better illumination of the velocity model than conventional FWI. Using a new objective function, we optimized the source image and velocity model, including multiscattered energy, simultaneously. Because we conducted our method in the frequency domain with a complex source function including spatial and wavelet information, we mitigate the uncertainties of the source wavelet and source origin time. Inversion results from the Marmousi model indicate that by taking advantage of multiscattered energy and starting from a reasonably acceptable frequency (a single source at 3 Hz and multiple sources at 5 Hz), our method yields better inverted velocity models and source images compared with conventional FWI.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

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