Gaussian beam imaging of fractures near the wellbore using sonic logging tools after removing dispersive borehole waves

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. D133-D143
Author(s):  
David Li ◽  
Xiao Tian ◽  
Hao Hu ◽  
Xiao-Ming Tang ◽  
Xinding Fang ◽  
...  
Keyword(s):  
Gaussian Beam ◽  
Field Data ◽  
Gas Production ◽  
Dipole Source ◽  
Data Sets ◽  
Data Set ◽  
Receiver Array ◽  
Gaussian Beam Migration ◽  
Sonic Logging ◽  
Borehole Waves

The ability to image near-wellbore fractures is critical for wellbore integrity monitoring as well as for energy production and waste disposal. Single-well imaging uses a sonic logging instrument consisting of a source and a receiver array to image geologic structures around a wellbore. We use cross-dipole sources because they can excite waves that can be used to image structures farther away from the wellbore than traditional monopole sources. However, the cross-dipole source also will excite large-amplitude, slowly propagating dispersive waves along the surface of the borehole. These waves will interfere with the formation reflection events. We have adopted a new fracture imaging procedure using sonic data. We first remove the strong amplitude borehole waves using a new nonlinear signal comparison method. We then apply Gaussian beam migration to obtain high-resolution images of the fractures. To verify our method, we first test our method on synthetic data sets modeled using a finite-difference approach. We then validate our method on a field data set collected from a fractured natural gas production well. We are able to obtain high-quality images of the fractures using Gaussian beam migration compared with Kirchhoff migration for the synthetic and field data sets. We also found that a low-frequency source (around 1 kHz) is needed to obtain a sharp image of the fracture because high-frequency wavefields can interact strongly with the fluid-filled borehole.

Least-squares Gaussian beam migration

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. S87-S100 ◽  
Author(s):  
Hao Hu ◽  
Yike Liu ◽  
Yingcai Zheng ◽  
Xuejian Liu ◽  
Huiyi Lu
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Spatial Resolution ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Seismic Acquisition ◽  
Gaussian Beam Migration ◽  
Least Squares Migration

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

A periodically varying code for improving deblending of simultaneous sources in marine acquisition

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V213-V225 ◽  
Author(s):  
Shaohuan Zu ◽  
Hui Zhou ◽  
Yangkang Chen ◽  
Shan Qu ◽  
Xiaofeng Zou ◽  
...  
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Ocean Bottom ◽  
Data Set ◽  
Acceptable Model ◽  
Model Subspace ◽  
New Form ◽  
Simultaneous Source ◽  
Practical Field

We have designed a periodically varying code that can avoid the problem of the local coherency and make the interference distribute uniformly in a given range; hence, it was better at suppressing incoherent interference (blending noise) and preserving coherent useful signals compared with a random dithering code. We have also devised a new form of the iterative method to remove interference generated from the simultaneous source acquisition. In each iteration, we have estimated the interference using the blending operator following the proposed formula and then subtracted the interference from the pseudodeblended data. To further eliminate the incoherent interference and constrain the inversion, the data were then transformed to an auxiliary sparse domain for applying a thresholding operator. During the iterations, the threshold was decreased from the largest value to zero following an exponential function. The exponentially decreasing threshold aimed to gradually pass the deblended data to a more acceptable model subspace. Two numerically blended synthetic data sets and one numerically blended practical field data set from an ocean bottom cable were used to demonstrate the usefulness of our proposed method and the better performance of the periodically varying code over the traditional random dithering code.

Least-squares Gaussian beam migration in viscoacoustic media

Geophysics ◽  
2020 ◽  
Vol 86 (1) ◽  
pp. S17-S28
Author(s):  
Yubo Yue ◽  
Yujin Liu ◽  
Yaonan Li ◽  
Yunyan Shi
Keyword(s):  
Green’S Function ◽  
Least Squares ◽  
Gaussian Beam ◽  
Green's Function ◽  
Seismic Waves ◽  
Image Resolution ◽  
Data Sets ◽  
Phase Dispersion ◽  
Using Data ◽  
Gaussian Beam Migration

Because of amplitude decay and phase dispersion of seismic waves, conventional migrations are insufficient to produce satisfactory images using data observed in highly attenuative geologic environments. We have developed a least-squares Gaussian beam migration method for viscoacoustic data imaging, which can not only compensate for amplitude decay and phase dispersion caused by attenuation, but it can also improve image resolution and amplitude fidelity through linearized least-squares inversion. We represent the viscoacoustic Green’s function by a summation of Gaussian beams, in which an attenuation traveltime is incorporated to simulate or compensate for attenuation effects. Based on the beam representation of the Green’s function, we construct the viscoacoustic Born forward modeling and adjoint migration operators, which can be effectively evaluated by a time-domain approach based on a filter-bank technique. With the constructed operators, we formulate a least-squares migration scheme to iteratively solve for the optimal image. Numerical tests on synthetic and field data sets demonstrate that our method can effectively compensate for the attenuation effects and produce images with higher resolution and more balanced amplitudes than images from acoustic least-squares Gaussian beam migration.

Cooperative constrained inversion of multiple electromagnetic data sets

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. B173-B185 ◽  
Author(s):  
Michael S. McMillan ◽  
Douglas W. Oldenburg
Keyword(s):  
Field Data ◽  
Gold Mineralization ◽  
Upper And Lower Bounds ◽  
Data Sets ◽  
Bound Constraints ◽  
Data Set ◽  
Near Surface ◽  
Resistivity Measurements ◽  
Inversion Model ◽  
Resistivity Model

We evaluated a method for cooperatively inverting multiple electromagnetic (EM) data sets with bound constraints to produce a consistent 3D resistivity model with improved resolution. Field data from the Antonio gold deposit in Peru and synthetic data were used to demonstrate this technique. We first separately inverted field airborne time-domain EM (AEM), controlled-source audio-frequency magnetotellurics (CSAMT), and direct current resistivity measurements. Each individual inversion recovered a resistor related to gold-hosted silica alteration within a relatively conductive background. The outline of the resistor in each inversion was in reasonable agreement with the mapped extent of known near-surface silica alteration. Variations between resistor recoveries in each 3D inversion model motivated a subsequent cooperative method, in which AEM data were inverted sequentially with a combined CSAMT and DC data set. This cooperative approach was first applied to a synthetic inversion over an Antonio-like simulated resistivity model, and the inversion result was both qualitatively and quantitatively closer to the true synthetic model compared to individual inversions. Using the same cooperative method, field data were inverted to produce a model that defined the target resistor while agreeing with all data sets. To test the benefit of borehole constraints, synthetic boreholes were added to the inversion as upper and lower bounds at locations of existing boreholes. The ensuing cooperative constrained synthetic inversion model had the closest match to the true simulated resistivity distribution. Bound constraints from field boreholes were then calculated by a regression relationship among the total sulfur content, alteration type, and resistivity measurements from rock samples and incorporated into the inversion. The resulting cooperative constrained field inversion model clearly imaged the resistive silica zone, extended the area of interpreted alteration, and also highlighted conductive zones within the resistive region potentially linked to sulfide and gold mineralization.

scholarly journals Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S197-S205 ◽  
Author(s):  
Zhaolun Liu ◽  
Abdullah AlTheyab ◽  
Sherif M. Hanafy ◽  
Gerard Schuster
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
3D Data ◽  
Strong Surface ◽  
Migration Method ◽  
Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

A method for adequate selection of training data sets to reconstruct seismic field data using a convolutional U-Net

Geophysics ◽  
2021 ◽  
pp. 1-103
Author(s):  
Jiho Park ◽  
Jihun Choi ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Data Analysis ◽  
Seismic Data ◽  
Field Data ◽  
Training Data ◽  
Data Reconstruction ◽  
Data Sets ◽  
Training Set ◽  
Data Set ◽  
Seismic Data Reconstruction ◽  
Target Data

Deep learning (DL) methods are recently introduced for seismic signal processing. Using DL methods, many researchers have adopted these novel techniques in an attempt to construct a DL model for seismic data reconstruction. The performance of DL-based methods depends heavily on what is learned from the training data. We focus on constructing the DL model that well reflect the features of target data sets. The main goal is to integrate DL with an intuitive data analysis approach that compares similar patterns prior to the DL training stage. We have developed a two-sequential method consisting of two stage: (i) analyzing training and target data sets simultaneously for determining target-informed training set and (ii) training the DL model with this training data set to effectively interpolate the seismic data. Here, we introduce the convolutional autoencoder t-distributed stochastic neighbor embedding (CAE t-SNE) analysis that can provide the insight into the results of interpolation through the analysis of both the training and target data sets prior to DL model training. The proposed method were tested with synthetic and field data. Dense seismic gathers (e.g. common-shot gathers; CSGs) were used as a labeled training data set, and relatively sparse seismic gather (e.g. common-receiver gathers; CRGs) were reconstructed in both cases. The reconstructed results and SNRs demonstrated that the training data can be efficiently selected using CAE t-SNE analysis and the spatial aliasing of CRGs was successfully alleviated by the trained DL model with this training data, which contain target features. These results imply that the data analysis for selecting target-informed training set is very important for successful DL interpolation. Additionally, the proposed analysis method can also be applied to investigate the similarities between training and target data sets for another DL-based seismic data reconstruction tasks.

Least-squares Gaussian beam migration in elastic media

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S329-S340 ◽  
Author(s):  
Yubo Yue ◽  
Paul Sava ◽  
Zhongping Qian ◽  
Jidong Yang ◽  
Zhen Zou
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Computational Cost ◽  
Synthetic Data ◽  
Preconditioned Conjugate Gradient ◽  
Imaging Method ◽  
Data Sets ◽  
Isotropic Media ◽  
Gaussian Beam Migration ◽  
Multicomponent Data

Gaussian beam migration (GBM) is an effective imaging method that has the ability to image multiple arrivals while preserving the advantages of ray-based methods. We have extended this method to linearized least-squares imaging for elastic waves in isotropic media. We have dynamically transformed the multicomponent data to the principal components of different wave modes using the polarization information available in the beam migration process, and then we use Gaussian beams as wavefield propagator to construct the forward modeling and adjoint migration operators. Based on the constructed operators, we formulate a least-squares migration scheme that is iteratively solved using a preconditioned conjugate gradient method. With this method, we can obtain crosstalk-attenuated multiwave images with better subsurface illumination and higher resolution than those of the conventional elastic Gaussian beam migration. This method also allows us to achieve a good balance between computational cost and imaging accuracy, which are both important requirements for iterative least-squares migrations. Numerical tests on two synthetic data sets demonstrate the validity and effectiveness of our proposed method.

scholarly journals Dip-angle image gather computation using the Poynting vector in elastic reverse time migration and their application for noise suppression

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. S159-S169 ◽  
Author(s):  
Qiancheng Liu
Keyword(s):  
Field Data ◽  
Noise Suppression ◽  
Poynting Vector ◽  
Data Sets ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Numerical Examples ◽  
Time Migration ◽  
Dip Angle

Angle-domain common-imaging gathers (ADCIGs) are important in analyzing the subsurface discontinuities where reflection waves take place. In elastic reverse time migration (ERTM), dip-angle ADCIGs can be computed postmigration via subsurface offset extension. We have obtained dip-angle ADCIG premigration in ERTM by using the Poynting vector, which was easy to compute during wavefield propagation. The reflection normal of PP imaging is the bisector of the scattering angle, whereas that of PS imaging is not. We derive formulas for PP and PS dip-angle estimations, respectively, with some straightforward vector operations. Similar to the subsurface-offset one, our method also outputs dip-angle ADCIGs with the appearance of blocky horizontal coherence. According to local semblance analysis, the signal with a better horizontal coherence promises a higher semblance score, and vice versa. We can thus design a specular filter to suppress incoherent noises according to their corresponding local semblance scores. We validate our methods with numerical examples. The Graben and Marmousi data sets show that our methods work effectively in dip-angle ADCIG computation and the following noise suppression in ERTM. We also examine our methods with one field data set.

scholarly journals Adaptive waveform inversion: Practice

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. R447-R461 ◽  
Author(s):  
Lluís Guasch ◽  
Michael Warner ◽  
Céline Ravaut
Keyword(s):  
Field Data ◽  
Waveform Inversion ◽  
Data Sets ◽  
Data Set ◽  
Depth Migration ◽  
The North ◽  
The Difference ◽  
Starting Model ◽  
Kirchhoff Depth Migration ◽  
Reflection Tomography

Adaptive waveform inversion (AWI) reformulates the misfit function used to perform full-waveform inversion (FWI), so that it no longer contains local minima related to cycle skipping. It does this by finding a model that drives the ratio of the predicted and observed data sets to unity rather than driving the difference between these two data sets to zero as is the case for conventional FWI. We apply AWI to a 3D field data set acquired over a pervasive gas cloud in the North Sea, comparing its performance with that of conventional FWI in a variety of circumstances. When starting inversion from 3 Hz, and using a good starting model obtained from reflection tomography, FWI and AWI generate similar models although the FWI result contains edge artifacts that are not produced by AWI. However, when the starting frequency is increased to approximately 6 Hz, or when the starting model is less accurate, FWI fails to recover a good model whereas AWI continues to converge. When both of these conditions apply, FWI fails comprehensively, leading to a model that is significantly worse than the starting model, whereas the AWI result remains largely unaffected. We applied Kirchhoff depth migration to the fully-processed data using the FWI result obtained following reflection tomography, and using the AWI result obtained from a simple one-dimensional starting model. We use the resulting migrated volumes, together with measures of residual moveout throughout the volume, to show that the AWI result from a simple starting model is at least as good as the FWI result obtained following tomography. We conclude that AWI is robust in the presence of cycle skipping on this 3D field data set, and can proceed successfully from a less-accurate starting model, and from a higher starting frequency, in circumstances in which FWI fails completely.

scholarly journals Mitigating elastic effects in marine 3-D full-waveform inversion

2019 ◽  
Vol 220 (3) ◽  
pp. 2089-2104
Author(s):  
Òscar Calderón Agudo ◽  
Nuno Vieira da Silva ◽  
George Stronge ◽  
Michael Warner
Keyword(s):  
Wave Equation ◽  
Field Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Data Sets ◽  
Acoustic Wave Equation ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform

SUMMARY The potential of full-waveform inversion (FWI) to recover high-resolution velocity models of the subsurface has been demonstrated in the last decades with its application to field data. But in certain geological scenarios, conventional FWI using the acoustic wave equation fails in recovering accurate models due to the presence of strong elastic effects, as the acoustic wave equation only accounts for compressional waves. This becomes more critical when dealing with land data sets, in which elastic effects are generated at the source and recorded directly by the receivers. In marine settings, in which sources and receivers are typically within the water layer, elastic effects are weaker but can be observed most easily as double mode conversions and through their effect on P-wave amplitudes. Ignoring these elastic effects can have a detrimental impact on the accuracy of the recovered velocity models, even in marine data sets. Ideally, the elastic wave equation should be used to model wave propagation, and FWI should aim to recover anisotropic models of velocity for P waves (vp) and S waves (vs). However, routine three-dimensional elastic FWI is still commercially impractical due to the elevated computational cost of modelling elastic wave propagation in regions with low S-wave velocity near the seabed. Moreover, elastic FWI using local optimization methods suffers from cross-talk between different inverted parameters. This generally leads to incorrect estimation of subsurface models, requiring an estimate of vp/vs that is rarely known beforehand. Here we illustrate how neglecting elasticity during FWI for a marine field data set that contains especially strong elastic heterogeneities can lead to an incorrect estimation of the P-wave velocity model. We then demonstrate a practical approach to mitigate elastic effects in 3-D yielding improved estimates, consisting of using a global inversion algorithm to estimate a model of vp/vs, employing matching filters to remove elastic effects from the field data, and performing acoustic FWI of the resulting data set. The quality of the recovered models is assessed by exploring the continuity of the events in the migrated sections and the fit of the latter with the recovered velocity model.

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