Use of AVOA data to estimate fluid indicator in a vertically fractured medium

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. C15-C24 ◽  
Author(s):  
Ranjit K. Shaw ◽  
Mrinal K. Sen
Keyword(s):  
Reflection Coefficient ◽  
Random Noise ◽  
Crack Density ◽  
Synthetic Data ◽  
Quantitative Measure ◽  
P Wave ◽  
Fluid Content ◽  
Data Inversion ◽  
Microstructural Property ◽  
Fractured Medium

Microstructural attributes of cracks and fractures, such as crack density, aspect ratio, and fluid infill, determine the elastic properties of a medium containing a set of parallel, vertical fractures. Although the tangential weakness [Formula: see text] of the fractures does not vary with the fluid content, the normal weakness [Formula: see text] exhibits significant dependence on fluid infill. Based on linear-slip theory, we used the ratio [Formula: see text] — termed the fluid indicator — as a quantitative measure of the fluid content in the fractures, with g representing the square of the ratio of S- and P-wave velocity in the unfractured medium. We used a Born formalism to derive the sensitivity to fracture weakness of PP- and PS-reflection coefficients for an interface separating an unfractured medium from a vertically fractured medium. Our formulae reveal that the PP-reflection coefficient does not depend on the 2D microcorrugation/surface roughness with ridges and valleys parallel to the fracture strike, whereas the PS-reflection coefficient is sensitive to this microstructural property of the fractures. Based on this formulation, we developed a method to compute the fluid indicator from wide-azimuth PP-AVOA data. Inversion of synthetic data corrupted with 10% random noise reliably estimates the normal and tangential fracture weaknesses and hence the fluid indicator can be determined accurately when the fractures are liquid-filled or partially saturated. As the gas saturation in the fractures increases, the quality of inversion becomes poorer. Errors of 15%–20% in g do not affect the estimation of fluid indicator significantly in case of liquid infill or partial saturation. However, for gas-saturated fractures, incorrect values of g may have a significant effect on fluid-indicator estimates.

Effects of fractures on NMO velocities and P‐wave azimuthal AVO response

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 711-726 ◽  
Author(s):  
Feng Shen ◽  
Xiang Zhu ◽  
M. Nafi Toksöz
Keyword(s):  
Aspect Ratio ◽  
Fractured Reservoirs ◽  
Crack Density ◽  
P Wave ◽  
Fractured Reservoir ◽  
Fracture Density ◽  
Fracture Detection ◽  
Fluid Content ◽  
Fracture Parameters ◽  
Fractured Medium

This paper attempts to explain the relationships between fractured medium properties and seismic signatures and distortions induced by geology‐related influences on azimuthal AVO responses. In the presence of vertically aligned fractures, the relationships between fracture parameters (fracture density, fracture aspect ratio, and saturated fluid content) and their seismic signatures are linked with rock physics models of fractured media. The P‐wave seismic signatures studied in this paper include anisotropic parameters (δ(v), (v), and γ(v)), NMO velocities, and azimuthal AVO responses, where δ(v) is responsible for near‐vertical P‐wave velocity variations, (v) defines P‐wave anisotropy, and γ(v) governs the degree of shearwave splitting. The results show that in gas‐saturated fractures, anisotropic parameters δ(v) and (v) vary with fracture density alone. However, in water‐saturated fractures δ(v) and (v) depend on fracture density and crack aspect ratio and are also related to Vp/VS and Vp of background rocks, respectively. Differing from δ(v) and (v), γ(v) is the parameter most related to crack density. It is insensitive to the saturated fluid content and crack aspect ratio. The P‐wave NMO velocities in horizontally layered media are a function of δ(v), and their properties are comparable with those of δ(v). Results from 3‐D finite‐difference modeling show that P‐wave azimuthal AVO variations do not necessarily correlate with the magnitude of fracture density. Our studies reveal that, in addition to Poisson's ratio, other elastic properties of background rocks have an effect on P‐wave azimuthal AVO variations. Varying the saturated fluid content of fractures can lead to azimuthal AVO variations and may greatly change azimuthal AVO responses. For a thin fractured reservoir, a tuning effect related to seismic wavelength and reservoir thickness can result in variations in AVO gradients and in azimuthal AVO variations. Results from instantaneous frequency and instantaneous bandwidth indicate that tuning can also lead to azimuthal variations in the rates of changes of the phase and amplitude of seismic waves. For very thin fractured reservoirs, the effect of tuning could become dominant. Our numerical results show that AVO gradients may be significantly distorted in the presence of overburden anisotropy, which suggests that the inversion of fracture parameters based on an individual AVO response would be biased unless this influence were corrected. Though P‐wave azimuthal AVO variations could be useful for fracture detection, the combination of other types of data is more beneficial than using P‐wave amplitude signatures alone, especially for the quantitative characterization of a fractured reservoir.

Model‐based inversion of amplitude‐variations‐with‐offset data using a genetic algorithm

Geophysics ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 939-954 ◽  
Author(s):  
Subhashis Mallick
Keyword(s):  
Reflection Coefficient ◽  
Acoustic Impedance ◽  
Poisson’S Ratio ◽  
Input Data ◽  
Synthetic Data ◽  
Single Layer ◽  
P Wave ◽  
Poisson's Ratio ◽  
Reasonable Accuracy ◽  
Avo Inversion

I cast the inversion of amplitude‐variation‐with‐offset (AVO) data into the framework of Bayesian statistics. Under such a framework, the model parameters and the physics of the forward problem are used to generate synthetic data. These synthetic data are then matched with the observed data to obtain an a‐posteriori probability density (PPD) function in the model space. The genetic algorithm (GA) uses a directed random search technique to estimate the shape of the PPD. Unlike the classical inversion methods, GA does not depend upon the choice of an initial model and is well suited for the AVO inversion. For the single‐layer AVO inversion where the amplitudes from a single reflection event are inverted, GA estimates the normal incidence reflection coefficient [Formula: see text] and the contrast of the Poisson’s ratio (Δσ) to reasonable accuracy, even when the signal‐to‐noise ratio is poor. Comparisons of single‐layer amplitude inversion using synthetic data demonstrate that GA inversion obtains more accurate results than does the least‐squares fit to the approximate reflection coefficients as is usually practiced in the industry. In the multilayer AVO waveform inversion, all or a part of the prestack data are inverted. Inversion of this type is nonunique for the estimation of the absolute values of velocities, Poisson’s ratios, and densities. However, by applying simplified approximations to the P‐wave reflection coefficient, I verify that [Formula: see text], the contrast in the acoustic impedance (ΔA), and the gradient in the reflection coefficient (G), can be estimated from such an inversion. From the GA estimated values of [Formula: see text], ΔA, and G, and from reliable estimates of velocity and Poisson’s ratio at the start time of the input data, an inverted model can be generated. I apply this procedure to marine data and demonstrate that the the synthetics computed from such an inverted model match the input data to reasonable accuracy. Comparison of the log data from a nearby well shows that the GA inversion obtains both the low‐ and the high‐frequency trends (within the bandwidth of seismic resolution) of the P‐wave acoustic impedance. In addition to P‐wave acoustic impedance, GA also obtains an estimate of the Poisson’s ratio, an extremely important parameter for the direct detection of hydrocarbons from seismic data.

Modeling sensitivity of 3D, 9-C wide azimuth data to changes in fluid content and crack density in cracked reservoirs

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. T155-T165 ◽  
Author(s):  
Herurisa Rusmanugroho ◽  
George A. McMechan
Keyword(s):  
Practical Importance ◽  
Crack Density ◽  
Maximum Amplitude ◽  
P Wave ◽  
Staggered Grid ◽  
Wave Reflections ◽  
S Wave ◽  
Reservoir Properties ◽  
Fluid Content ◽  
Isotropic Media

The volume density of cracks and the fluids contained in them are salient aspects of characterization of cracked reservoirs. Thus, it is of practical importance to investigate whether variations in these reservoir properties are detectable in seismic observations. Eighth-order staggered-grid, 3D finite-difference simulations generate nine-component amplitude variations with offset and azimuth (AVOAZ) for reflections from the top of a vertically cracked zone embedded in an isotropic host. The T-matrix method is used to calculate elastic stiffness tensors. Responses for various crack densities and fluid contents show sensitivity to the spatial orientation of, and variation in, anisotropy. In isotropic media, when source and receiver components have the same orientation (such as XX and YY), reflection amplitude contours align approximately perpendicular to the particle motion. Mixed components (such as XY and YX) have amplitude patterns thatare symmetrical pairs of the same, or opposite, polarity on either side of the diagonal of the 9-C response matrix. In anisotropic media, AVOAZ data show the same basic patterns and symmetries as for isotropic media but with a superimposed tendency for alignment parallel to the strike of the vertical cracks. The data contain combined effects related to the source, receiver, and crack orientations. The sensitivity of data to changes in fluid content is quantified by comparing the differences between responses to various fluid conditions, to the maximum amplitude of oil-filled crack responses. For a crack density of 0.1, amplitude differences are [Formula: see text] for oil-dry and [Formula: see text] for oil-brine. The corresponding values for S-wave reflections are [Formula: see text] for oil-dry and [Formula: see text] for oil-brine. Amplitude changes caused by changing the oil-filled crack density from 0.1 to 0.2 are [Formula: see text] for P-wave reflections and [Formula: see text] for S-wave reflections. These differences are visible in AVOAZ data and are potentially diagnostic for reservoir characterization.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Multiscale fracture density analysis at Stromboli Volcano, Italy: implications to flank stability

2021 ◽  
Author(s):  
Thomas Alcock ◽  
Sergio Vinciguerra ◽  
Phillip Benson ◽  
Federico Vagnon
Keyword(s):  
Electrical Resistivity ◽  
Wave Velocity ◽  
Rift Zone ◽  
Pulse Generator ◽  
Average Length ◽  
Crack Density ◽  
P Wave ◽  
Fracture Density ◽  
Stromboli Volcano ◽  
P Wave Velocity

<p>Stromboli volcano has experienced four sector collapses over the past 13 thousand years, resulting in the formation of the Sciara del Fuoco (SDF) horseshoe-shaped depression and an inferred NE / SW striking rift zone across the SDF and the western sector of the island. These events have resulted in the formation of steep depressions on the slopes on the volcano where episodes of instability are continuously being observed and recorded. This study aims to quantify the fracture density inside and outside the rift zone to identify potential damaged zones that could reduce the edifice strength and promote fracturing. In order to do so we have carried out a multiscale analysis, by integrating satellite observations, field work and seismic and electrical resistivity analyses on cm scales blocks belonging to 11 lava units from the main volcanic cycles that have built the volcano edifice, ie. Paleostromboli, Nestromboli and Vancori. 0.5 m resolution Pleiades satellite data has been first used to highlight 23635 distinct linear features across the island. Fracture density has been calculated using Fracpaq based on the Mauldon et al (2001) method to determine the average fracture density of a given area on the basis of the average length of drawn segments within a predetermined circular area. 41.8 % of total fracture density is found around intrusions and fissures, with the summit area and the slopes of SDF having the highest average fracture density of 5.279  . Density, porosity, P- wave velocity in dry and wet conditions and electrical resistivity (in wet conditions) were measured  via an ultrasonic pulse generator and acquisition system (Pundit) and an on purpose built measuring quadrupole on cm scale blocks of lavas collected from both within and outside the proposed rift zone to assess the physical state and the crack damage of the different lava units.  Preliminary results show that P-wave velocity between ~ 2.25 km/s < Vp < 5km/s decreases with porosity while there is high variability electrical resistivity with 21.7 < ρ < 590 Ohm * m. This is presumably due to the lavas texture and the variable content of bubble/vesicles porosity and crack damage, that is reflected by an effective overall porosity between 0 and 9 %. Higher porosity is generally mirrored by lower p-wave velocity values. Neostromboli blocks show the most variability in both P-wave velocity and electrical resistivity. Further work will assess crack density throughout optical analyses and systematically investigate the UCS and elastic moduli. This integrated approach is expected to provide a multiscale fracture density and allow to develop further laboratory testing on how slip surfaces can evolve to a flank collapse at Stromboli.</p>

3-D elastic prestack, reverse‐time depth migration

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 597-609 ◽  
Author(s):  
Wen‐Fong Chang ◽  
George A. McMechan
Keyword(s):  
Finite Differences ◽  
Synthetic Data ◽  
P Wave ◽  
Common Source ◽  
Reverse Time ◽  
Depth Migration ◽  
Full Wave ◽  
Source Data ◽  
Recorded Data ◽  
Time Extrapolation

By combining and extending previous algorithms for 2-D prestack elastic migration and 3-D prestack acoustic migration, a full 3-D elastic prestack depth migration algorithm is developed. Reverse‐time extrapolation of the recorded data is by 3-D elastic finite differences; computation of the image time for each point in the 3-D volume is by 3-D acoustic finite differences. The algorithm operates on three‐component, vector‐wavefield common‐source data and produces three‐component vector reflectivity distributions. Converted P‐to‐S reflections are automatically imaged with the primary P‐wave reflections. There are no dip restrictions as the full wave equation is used. The algorithm is illustrated by application to synthetic data from three models; a flat reflector, a dipping truncated wedge overlying a flat reflector, and the classical French double dome and fault model.

scholarly journals Rapid estimation of earthquake locations using waveform traveltimes

2019 ◽  
Vol 217 (3) ◽  
pp. 1727-1741 ◽  
Author(s):  
D W Vasco ◽  
Seiji Nakagawa ◽  
Petr Petrov ◽  
Greg Newman
Keyword(s):  
Standard Error ◽  
Random Noise ◽  
Grid Point ◽  
Synthetic Data ◽  
Velocity Model ◽  
Source Location ◽  
Model Errors ◽  
Average Deviation ◽  
Origin Time ◽  
Data Set

SUMMARY We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

Sign in / Sign up

Export Citation Format

Share Document