scholarly journals From surface seismic data to reservoir elastic parameters using a full-wavefield redatuming approach

2019 ◽  
Vol 221 (1) ◽  
pp. 115-128 ◽  
Author(s):  
Aayush Garg ◽  
D J Verschuur
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Impulse Responses ◽  
Target Domain ◽  
Elastic Parameters ◽  
Full Waveform ◽  
Parameters Inversion ◽  
Local Input

SUMMARY Traditionally, reservoir elastic parameters inversion suffers from the overburden multiple scattering and transmission imprint in the local input data used for the target-oriented inversion. In this paper, we present a full-wavefield approach, called reservoir-oriented joint migration inversion (JMI-res), to estimate the high-resolution reservoir elastic parameters from surface seismic data. As a first step in JMI-res, we reconstruct the fully redatumed data (local impulse responses) at a suitable depth above the reservoir from the surface seismic data, while correctly accounting for the overburden interal multiples and transmission losses. Next, we apply a localized elastic full waveform inversion on the estimated impulse responses to get the elastic parameters. We show that JMI-res thus provides much more reliable local target impulse responses, thus yielding high-resolution elastic parameters, compared to a standard redatuming procedure based on time reversal of data. Moreover, by using this kind of approach we avoid the need to apply a full elastic full waveform inversion-type process for the whole subsurface, as within JMI-res elastic full waveform inversion is only restricted to the reservoir target domain.

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.

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.

scholarly journals High Resolution Anisotropic Earth Model Building on Conventional Seismic Data Using Full Waveform Inversion: A Case Study Offshore Australia

2015 ◽  
Vol 2015 (1) ◽  
pp. 1-5
Author(s):  
Bee Jik Lim ◽  
Denes Vigh ◽  
Stephen Alwon ◽  
Saeeda Hydal ◽  
Martin Bayly ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Model Building ◽  
Waveform Inversion ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Full Waveform

High Resolution Imaging of the South Hikurangi Subduction Zone, New Zealand, Using 2‐D Full‐Waveform Inversion

2020 ◽  
Author(s):  
Adnan Djeffal ◽  
Ingo Pecher ◽  
Satish Singh ◽  
Jari Kaipio
Keyword(s):  
New Zealand ◽  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Seismic Profile ◽  
Full Waveform Inversion ◽  
Traveltime Tomography ◽  
Full Waveform ◽  
Velocity Images

<p>Large quantities of fluids are predicted to be expelled from compacting sediments on subduction margins. Fluid expulsion is thought to be focussed, but its exact locations are usually constrained on very small scales and rarely can be resolved using velocity images obtained from traditional velocity analysis and ray-based tomography because of their resolution and accuracy limitation. However, with recent advancement in computing power, the full waveform inversion (FWI) is a powerful alternative to those traditional approaches as it uses phase and amplitude information contained in seismic data to yield a high-resolution velocity model of the subsurface.</p><p>Here, we applied elastic FWI along an 85 Km long 2D multichannel seismic profile on the southern Hikurangi margin, New Zealand. Our processing sequence includes: (1) downward continuation, (2) 2D traveltime tomography, and (3) full waveform inversion of wide-angle seismic data. We will present the final high-resolution velocity model and our interpretation of the fluid flow regimes associated with both the deforming overriding plate and the subducting plate.</p>

scholarly journals High-resolution reservoir characterization using deep learning-aided elastic full-waveform inversion: The North Sea field data example

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA137-WA146
Author(s):  
Zhen-dong Zhang ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
North Sea ◽  
Seismic Data ◽  
Field Data ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Oil Field ◽  
Full Waveform Inversion ◽  
Full Waveform

Reservoir characterization is an essential component of oil and gas production, as well as exploration. Classic reservoir characterization algorithms, deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface (e.g., assuming linearized reflection coefficients). Elastic full-waveform inversion (FWI), which aims to match the waveforms of prestack seismic data, potentially provides more accurate high-resolution reservoir characterization from seismic data. However, FWI can easily fail to characterize deep-buried reservoirs due to illumination limitations. We have developed a deep learning-aided elastic FWI strategy using observed seismic data and available well logs in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which correspond to the subsurface facies distribution. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic FWI aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, indicates that the use of facies as prior knowledge helps resolve the deep-buried reservoir target better than the use of only seismic data.

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