Journal articles: 'AVO'

1

Ross, Christopher P. "Comparison of popular AVO attributes, AVO inversion, and calibrated AVO predictions." Leading Edge 21, no. 3 (March 2002): 244–52. http://dx.doi.org/10.1190/1.1463776.

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2

Colbeck, Carol L., and Robert Drago. "Accept avo." Change: The Magazine of Higher Learning 37, no. 6 (November 2005): 10–17. http://dx.doi.org/10.3200/chng.37.6.10-17.

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3

Simmons, James L., and Milo M. Backus. "Waveform‐based AVO inversion and AVO prediction‐error." GEOPHYSICS 61, no. 6 (November 1996): 1575–88. http://dx.doi.org/10.1190/1.1444077.

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A practical approach to linear prestack seismic inversion in the context of a locally 1-D earth is employed to use amplitude variation with offset (AVO) information for the direct detection in hydrocarbons. The inversion is based on the three‐term linearized approximation to the Zoeppritz equations. The normal‐incidence compressional‐wave reflection coefficient [Formula: see text] models the background reflectivity in the absence of hydrocarbons and incorporates the mudrock curve and Gardner’s equation. Prediction‐error parameters, [Formula: see text] and [Formula: see text], represent perturbations in the normal‐incidence shear‐wave reflection coefficient and the density contribution to the normal incidence reflectivity, respectively, from that predicted by the mudrock curve and Gardner’s equation. This prediction‐error approach can detect hydrocarbons in the absence of an overall increase in AVO, and in the absence of bright spots, as expected in theory. Linear inversion is applied to a portion of a young, Tertiary, shallow‐marine data set that contains known hydrocarbon accumulations. Prestack data are in the form of angle stack, or constant offset‐to‐depth ratio, gathers. Prestack synthetic seismograms are obtained by primaries‐only ray tracing using the linearized approximation to the Zoeppritz equations to model the reflection amplitudes. Where the a priori assumptions hold, the data are reproduced with a single parameter [Formula: see text]. Hydrocarbons are detected as low impedance relative to the surrounding shales and the downdip brine‐filled reservoir on [Formula: see text], also as positive perturbations (opposite polarity relative to [Formula: see text]) on [Formula: see text] and [Formula: see text]. The maximum perturbation in [Formula: see text] from the normal‐incidence shear‐wave reflection coefficient predicted by the a priori assumptions is 0.08. Hydrocarbon detection is achieved, although the overall seismic response of a gas‐filled thin layer shows a decrease in amplitude with offset (angle). The angle‐stack data (70 prestack ensembles, 0.504–1.936 s time range) are reproduced with a data residual that is 7 dB down. Reflectivity‐based prestack seismograms properly model a gas/water contact as a strong increase in AVO and a gas‐filled thin layer as a decrease in AVO.

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Beretta, Matteo Mario, Giancarlo Bernasconi, and Giuseppe Drufuca. "AVO and AVA inversion for fractured reservoir characterization." GEOPHYSICS 67, no. 1 (January 2002): 300–306. http://dx.doi.org/10.1190/1.1451802.

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Seismic wave reflection amplitudes are used to detect fluids and fracture properties in reservoirs. This paper studies the characterization of a vertically fractured fluid‐filled reservoir by analyzing the reflection amplitudes of P‐waves with varying incident and azimuthal angles. The reservoir is modeled as a horizontal transversely isotropic medium embedded in an isotropic background, and the linearized P‐waves reflection coefficient are considered. The conditioning of the inverse problem is analyzed, and fracture density is found to be the best conditioned parameter. Using diffraction tomography under the Born approximation, an inversion procedure is proposed in the transformed k–ω domain to detect fracture density variations within the reservoir. Seismic data are rearranged in pairs of incident and reflected plane waves, enlightening only one spectral component of the fracture density field at a time. Only the observable spectral components are inverted. Moreover, working in the transformed domain, picking reflection amplitudes is not required. An example of the inversion applied to a synthetic data set is presented. The limitation of source and receiver numbers and the finite bandwidth of the wavelet produce a loss of resolution, but the overall fracture density variations are recovered correctly.

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5

Mahob, Patrice Nsoga, and John P. Castagna. "AVO polarization and hodograms: AVO strength and polarization product." GEOPHYSICS 68, no. 3 (May 2003): 849–62. http://dx.doi.org/10.1190/1.1581037.

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An alternative approach to identifying amplitude‐variation‐with offset (AVO) anomalies is to consider the AVO polarization in the AVO intercept–AVO gradient (A‐B) plane. This method does not require deviations or separations from a background trend exhibited in traditional crossplots such as intercept‐gradient (A‐B) or near trace–far trace (N‐F). A benefit of the hodogram or polarization method is that the wavelet is taken into consideration. Crossplotted intercept and gradient are polarized along a “background trend” for nonanomalous events and at angles different from the “background trend” for anomalous events. This allows recognition of anomalous behavior otherwise buried in a background. Attributes resulting from this methodology include (1) the polarization angle, (2) the polarization angle difference, (3) the AVO strength, (4) the polarization product, and (5) the linear‐correlation coefficient. These different attributes can then be used to enhance AVO interpretation. Synthetic modeling for a succession of gas and brine layers encased in shale units indicates that the method can potentially be an effective hydrocarbon indicator. Application of the method to a real seismic dataset shows polarization anomalies associated with hydrocarbons.

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6

Ross, Christopher P. "Unbiased AVO crossplotting?" Leading Edge 35, no. 4 (April 2016): 338–44. http://dx.doi.org/10.1190/tle35040338.1.

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7

Ramos, Antonio C. B. "AVO processing calibration." Leading Edge 17, no. 8 (August 1998): 1075. http://dx.doi.org/10.1190/1.1438093.

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8

Ursin, Bjørn, and Bjørn Olav Ekren. "Robust AVO analysis." GEOPHYSICS 60, no. 2 (March 1995): 317–26. http://dx.doi.org/10.1190/1.1443768.

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Amplitude variation with offset (AVO) analysis is sensitive to errors in the moveout correction. To reduce moveout errors, AVO‐effects are estimated using time windows. For each time window, block‐NMO is used to avoid NMO‐stretch, and correction for residual NMO is performed by an iterative cross‐correlation technique. The data in a moveout‐corrected time window are modeled as a constant pulse multiplied by an amplitude function that is approximated by a polynomial in the offset coordinate. Conversion of data from offset to angle, or slowness, is therefore avoided. The seismic pulse and the polynomial coefficients are found by the least‐squares method. This is a separable least‐squares problem and the polynomial coefficients and the pulse can be estimated separately. The reduced nonlinear optimization problem for the coefficients is expressed as a Rayleigh quotient, providing the solution in one step. Once the coefficients have been found, the estimate of the pulse is computed by an explicit formula. This gives an efficient computational scheme. The method is demonstrated on a shallow seismic anomaly in the Barents Sea, offshore northern Norway.

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9

Ross, C. P. "Incomplete AVO near Salt Structures and Impact for AVO Analysis." Energy Exploration & Exploitation 10, no. 4-5 (September 1992): 335–53. http://dx.doi.org/10.1177/014459879201000410.

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Amplitude versus offset (AVO) measurements for deep hydrocarbon-bearing sands can be compromised when made in close proximity to a shallow salt piercement structure. Anomalous responses are observed, particularly on low acoustic impedance bright spots. Common-midpoint (CMP) data from key seismic profiles traversing the bright spots do not show the expected Class 3 offset responses as defined by Rutherford and Williams (1989). On these CMPs, significant decrease of far trace energy is observed. CMP data from other seismic profiles off-structure do exhibit the Class 3 offset responses, implying that structural complications may be interfering with the offset response. A synthetic AVO gather was generated using well log data, which supports the off-structure Class 3 responses, further reinforcing the concept of structurally-biased AVO responses. Acoustic, pseudo-spectral modelling of the structure substantiates the misleading AVO response. Pseudo-spectral modelling results suggest signal degradation observed on the far offsets is caused by wavefield refraction — a shadow zone, where the known hydrocarbon-bearing sands are not completely illuminated. Such shadow zones obscure the correct AVO response, which may have bearing on exploration and development.

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10

Tygel, Martin, Lúcio T. Santos, Jörg Schleicher, and Peter Hubral. "Kirchhoff imaging as a tool for AVO/AVA analysis." Leading Edge 18, no. 8 (August 1999): 940–45. http://dx.doi.org/10.1190/1.1438413.

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11

Sarkar, D., J. P. Castagna, and W. J. Lamb. "AVO and velocity analysis." GEOPHYSICS 66, no. 4 (July 2001): 1284–93. http://dx.doi.org/10.1190/1.1487076.

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Velocity analysis using semblance estimates velocities based on a constant amplitude model for seismograms and does not take amplitude variation with offset (AVO) into account. In the presence of AVO, the constant amplitude model becomes inaccurate, particularly for events which exhibit polarity reversals. An AVO sensitive velocity analysis procedure, which is a generalization of the traditional semblance method, can be devised by giving an offset dependence to the modeled seismograms. Incorporating AVO into velocity analysis requires additional parameters to describe the reflectivity. This results in reduced velocity precision. By introducing a regularization term which provides a controlled suppression of the contributions due to AVO effects, we describe an AVO sensitive velocity analysis algorithm that properly deals with events exhibiting polarity reversals or large amplitude variation with offset.

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12

Ross, Christopher P., and Daniel L. Kinman. "Nonbright‐spot AVO: Two examples." GEOPHYSICS 60, no. 5 (September 1995): 1398–408. http://dx.doi.org/10.1190/1.1443875.

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The use of amplitude variation with offset (AVO) attribute sections such as the product of the normal incidence trace (A) and the gradient trace (B) have been used extensively in bright spot AVO analysis and interpretation. However, while these sections have often worked well with low acoustic impedance bright spot responses, they are not reliable indicators of nonbright‐spot seismic anomalies. Analyzing nonbright‐spot seismic data with common AVO attribute sections will: (1) not detect the gas‐charged reservoir because of near‐zero acoustic impedance contrast between the sands and encasing shales, or (2) yield an incorrect (negative) AVO product if the normal incidence and gradient values are opposite in sign. We divide nonbright‐spot AVO offset responses into two subcategories: those with phase reversals and those without. An AVO analysis procedure for these anomalies is presented through two examples. The procedure exploits the nature of the prestack response, yielding a more definitive AVO attribute section, and this technique is adaptive to both subcategories of nonbright‐spot AVO responses. This technique identifies the presence of gas‐charged pore fluids within the reservoir when compared to a conventionally processed, relative amplitude seismic section with characteristically low amplitude responses for near‐zero acoustic impedance contrast sands.

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13

Russell, Brian H., David Gray, and Daniel P. Hampson. "Linearized AVO and poroelasticity." GEOPHYSICS 76, no. 3 (May 2011): C19—C29. http://dx.doi.org/10.1190/1.3555082.

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The technique of amplitude variation with offset (AVO) allows geoscientists to extract fluid and lithology information from the analysis of prestack seismic amplitudes. Various AVO parameterizations exist, all of which involve the sum of three weighted elastic-constant terms. In present-day AVO approaches, the weighting terms involve either knowledge of the incidence angle only, or knowledge of both the incidence angle and the in situ VP/VS ratio. We have used the theory of poroelasticity to derive a generalized AVO approximation that provides the estimation of fluid, rigidity, and density parameters. We have combined two previously independent AVO formulations, thus reducing, instead of adding to, the total number of formulations. This new approach requires knowledge of a third parameter to compute the weights: the dry-rock VP/VS ratio. We have derived a new equation and applied it to model and real data sets. The new formulation has allowed us to estimate fluid properties of the reservoir in a more direct manner than previous formulations.

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14

Balzer, Heike. "AVO forciert das Exportgeschäft." Lebensmittel Zeitung 73, no. 25 (2021): 48. http://dx.doi.org/10.51202/0947-7527-2021-25-048-2.

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15

Russell, Brian H., Laurence R. Lines, Keith W. Hirsche, Janusz Peron, and Daniel P. Hampson. "The AVO modelling volume." ASEG Extended Abstracts 2001, no. 1 (December 2001): 1–4. http://dx.doi.org/10.1071/aseg2001ab121.

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16

Russell, Brian H., Laurence R. Lines, Keith W. Hirsche, Janusz Peron, and Daniel P. Hampson. "The AVO Modelling Volume." Exploration Geophysics 32, no. 3-4 (September 2001): 264–70. http://dx.doi.org/10.1071/eg01264.

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17

Foster, Douglas J., Robert G. Keys, and F. David Lane. "Interpretation of AVO anomalies." GEOPHYSICS 75, no. 5 (September 2010): 75A3–75A13. http://dx.doi.org/10.1190/1.3467825.

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We investigate the effects of changes in rock and fluid properties on amplitude-variation-with-offset (AVO) responses. In the slope-intercept domain, reflections from wet sands and shales fall on or near a trend that we call the fluid line. Reflections from the top of sands containing gas or light hydrocarbons fall on a trend approximately parallel to the fluid line; reflections from the base of gas sands fall on a parallel trend on the opposing side of the fluid line. The polarity standard of the seismic data dictates whether these reflections from the top of hydrocarbon-bearing sands are below or above the fluid line. Typically, rock properties of sands and shales differ, and therefore reflections from sand/shale interfaces are also displaced from the fluid line. The distance of these trends from the fluid line depends upon the contrast of the ratio of P-wave velocity [Formula: see text] and S-wave velocity [Formula: see text]. This ratio is a function of pore-fluid compressibility and implies that distance from the fluid line increases with increasing compressibility. Reflections from wet sands are closer to the fluid line than hydrocarbon-related reflections. Porosity changes affect acoustic impedance but do not significantly impact the [Formula: see text] contrast. As a result, porosity changes move the AVO response along trends approximately parallel to the fluid line. These observations are useful for interpreting AVO anomalies in terms of fluids, lithology, and porosity.

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18

Sen, Mrinal K., and Paul L. Stoffa. "Genetic inversion of AVO." Leading Edge 11, no. 1 (January 1992): 27–29. http://dx.doi.org/10.1190/1.1436845.

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19

Castagna, John P. "Petrophysical imaging using AVO." Leading Edge 12, no. 3 (March 1993): 172–78. http://dx.doi.org/10.1190/1.1436939.

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20

Castagna, John P., and Herbert W. Swan. "Principles of AVO crossplotting." Leading Edge 16, no. 4 (April 1997): 337–44. http://dx.doi.org/10.1190/1.1437626.

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21

Mallick, Subhashis. "AVO and elastic impedance." Leading Edge 20, no. 10 (October 2001): 1094–104. http://dx.doi.org/10.1190/1.1487239.

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22

Buland, Arild, and Henning Omre. "Bayesian linearized AVO inversion." GEOPHYSICS 68, no. 1 (January 2003): 185–98. http://dx.doi.org/10.1190/1.1543206.

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A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3‐D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.

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23

Young, Roger A., and Robert D. LoPiccolo. "A comprehensive AVO classification." Leading Edge 22, no. 10 (October 2003): 1030–37. http://dx.doi.org/10.1190/1.1623645.

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24

Bakke, N. E., and B. Ursin. "Thin-bed AVO effects." Geophysical Prospecting 46, no. 6 (October 1998): 571–87. http://dx.doi.org/10.1046/j.1365-2478.1998.00101.x.

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25

Russell, Brian, Christopher Ross, and Larry Lines. "Neural networks and AVO." Leading Edge 21, no. 3 (March 2002): 268–314. http://dx.doi.org/10.1190/1.1885507.

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26

Gretener, Peter. "AVO and Poisson's ratio." Leading Edge 22, no. 1 (January 2003): 70–72. http://dx.doi.org/10.1190/1.1888165.

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27

Foster, Douglas J., Zeyu Zhao, Dhananjay Kumar, Danica Dralus, and Mrinal K. Sen. "Frequency-dependent AVO analysis." Leading Edge 39, no. 2 (February 2020): 84–91. http://dx.doi.org/10.1190/tle39020084.1.

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A method for generalizing the conventional amplitude variation with offset model from an isolated interface to a scattering reservoir interval is presented. The advantage of this new method is that it can provide enhanced detection of subtle reservoir and pore fluid properties. First- and second-order expressions for the reflected compressional wave energy from a specified heterogenous interval are given. These expressions are applied to two problems of interest for reservoir description. One application is discriminating low versus higher saturations of hydrocarbons, and the other is detecting the extent of vertical stratification within a reservoir. The first-order expression is used for determining hydrocarbon saturations, and the second-order expression is used for detecting the magnitude of fine-scale layering within a reservoir. Synthetic models and field data examples are used in demonstrating the applicability of the proposed method.

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28

Zhang, Zhao, Chen Bao, Reynaldo Cardona, John Castagna, Todd Dygert, Tapan Mukerji, Stephan Gelinsky, Brian Russell, Yuefeng Sun, and Shiyu Xu. "Introduction to special section: Rock properties from AVA/AVO analysis." Interpretation 8, no. 1 (February 1, 2020): SAi—SAiii. http://dx.doi.org/10.1190/int-2019-1212-spseintro.1.

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29

Reilly, Joseph M. "Wireline shear and AVO modeling: Application to AVO investigations of the Tertiary, U.K. Central North Sea." GEOPHYSICS 59, no. 8 (August 1994): 1249–60. http://dx.doi.org/10.1190/1.1443682.

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Numerous examples exist in the literature of the successes and failures of the application of amplitude variation with offset (AVO) “gas effect” theory in hydrocarbon exploration. The acquisition of wireline and VSP shear data is considered critical information for accurate calibration, quantification, modeling, and successful application of AVO analysis techniques in a particular geologic environment. Wireline compressional, wireline dipole shear, well vertical seismic profiling (VSP), and surface seismic data are used to investigate the AVO behavior of the major lithologic boundaries and hydrocarbon bearing zones. Within the study area, lithologic boundaries produce significant AVO anomalies that may be easily confused with Class III AVO gas effect. In addition, the principal economic target, oil bearing sands, does not appear to produce AVO gas effect anomalies; although a Class III AVO gas effect response is observed when as little as 2 m of free gas is present at the top of the reservoir. Confirmation of an (Class I, II, or III) AVO gas effect in these Tertiary sands can be significant for detecting and delineating reservoirs; however, excessive emphasis on the importance of AVO gas effect, or high amplitudes on relative amplitude seismic sections, will result in an underestimate of (oil) hydrocarbon potential within the region.

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Ross, Christopher P. "Effective AVO crossplot modeling: A tutorial." GEOPHYSICS 65, no. 3 (May 2000): 700–711. http://dx.doi.org/10.1190/1.1444769.

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The ability to crossplot attributes from a 3-D seismic volume permits a geophysicist to identify and high grade subsets of the 3-D volume that warrant detailed inspection. In the case of amplitude‐variation‐with‐offset (AVO) crossplotting, the seismic attributes are derived from CDP data. Crossplotting has become a fundamental process in AVO analysis, just as it is in petrophysical analysis. Comprehending the intricacies and selection of attributes is essential for successful AVO analysis and improved seismic interpretation. AVO crossplotting of modeled seismic data derived from well logs with the Biot‐Gassmann equations provides a basis for understanding fluid substitution effects on AVO attribute interactions when crossplotting. With these model‐based understandings, improved multi‐attribute interpretation processes can commence with AVO crossplotting of seismic volumes.

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31

Huuskonen, Christa, Mari Hämäläinen, Nooa Kivikangas, Timo Paavonen, Eeva Moilanen, and Ari Mennander. "Myocardial interaction of apixaban after experimental acute volume overload." Journal of International Medical Research 50, no. 11 (November 2022): 030006052211374. http://dx.doi.org/10.1177/03000605221137474.

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Objective Acute volume overload (AVO) induces early ischemia-like changes in intramyocardial arteries. We investigated whether the Factor Xa (FXa) inhibitor apixaban interacts with the myocardium early after AVO. Methods Fifty-five syngeneic Fisher rats underwent surgical abdominal aortocaval fistula to induce AVO. Among them, 17 rats were treated with apixaban (10 mg/kg/day). The myocardial outcome was studied using histological analysis and by measuring atrial natriuretic peptide (ANP) and matrix metalloprotease 9 (MMP9) gene expression. Results After 3 days, the total number of intramyocardial arteries was significantly increased in the AVO+apixaban (AVO+A) group compared with that in the AVO group (12.0 ± 1.2 and 10.2 ± 1.5, point score units, respectively). In the AVO+A group, there were significantly more edematous nuclei in myocardial arteries in the right and left ventricle compared with that in the AVO group. ANP and MMP9 expression levels continued to increase significantly in the AVO+A group compared with those in the AVO group. Conclusion Apixaban interacts with intramyocardial arteries in the left and right ventricles after AVO and ANP and MMP9 expression levels increased. Thus, the myocardial effect of Factor Xa inhibition needs to be monitored after AVO.

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Munadi, Suprajitno. "Avo Inversion Using Levenberg-Marquardt Optimization Technique." Scientific Contributions Oil and Gas 33, no. 2 (February 22, 2022): 98–105. http://dx.doi.org/10.29017/scog.33.2.812.

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AVO is not only well known as gas indicator over the last two decades but also more importantly, AVO provides us with a means for extracting petrophysical parameters from seismic data. Using AVO anomaly one can derive important petrophysical parameters such as Poisson’s ratio and S-wave velocity. By knowing S-wave velocity nearly all other petrophysical parameters can be calculated. An effective procedure for inverting AVO anomaly is presented in this paper. It avoids inefficient trial and error steps during the matching process between AVO anomaly and calculated AVO. This method uses Levenberg-Marquardt optimization technique.

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Soroka, William L., Thomas J. Fitch, Kirk H. Van Sickle, and Philip D. North. "Successful production application of 3‐D amplitude variation with offset: The lessons learned." GEOPHYSICS 67, no. 2 (March 2002): 379–90. http://dx.doi.org/10.1190/1.1468598.

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Amplitude variation with offset (AVO) analysis was successfully performed on a 3‐D prestack seismic volume. Important conclusions were that AVO results could improve field development and production, that 3‐D AVO results were more useful than 2‐D AVO results, and that reliable AVO results could be generated on land. The AVO results were used to help develop an infill drilling program to increase production. AVO information lowered the risk of finding hydrocarbons by helping to identify seismic events that had a higher probability of being gas‐saturated sands. The 3‐D seismic survey covered known gas zones and potential new reserves. The AVO calibration work showed that positive AVO gas responses (classes 2 and 3) were observed for 90% of the zones associated with known production. One 15‐ft‐thick gas reservoir below seismic resolution did not give a positive AVO anomaly. A well drilled to an untested zone displaying a positive AVO anomaly encountered commercial quantities of gas. Production from this new zone at the initial flow rate increased the total production rate in this 25‐year‐old field by >50%. The AVO method was shown to be applicable onshore and to provide useful results in more consolidated geologic environments with classes 2 and 3 AVO responses. For the successful use of AVO, greater effort and extra care in acquisition and processing were needed than in a normal seismic program.

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Mosher, Charles C., Timothy H. Keho, Arthur B. Weglein, and Douglas J. Foster. "The impact of migration on AVO." GEOPHYSICS 61, no. 6 (November 1996): 1603–15. http://dx.doi.org/10.1190/1.1444079.

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Amplitude variation with offset (AVO) analysis is often limited to areas where multidimensional propagation effects such as reflector dip and diffractions from faults can be ignored. Migration‐inversion provides a framework for extending the use of seismic amplitudes to areas where structural or stratigraphic effects are important. In this procedure, sources and receivers are downward continued into the earth using uncollapsed prestack migration. Instead of stacking the data as in normal migration, the prestack migrated data are used in AVO analysis or other inversion techniques to infer local earth properties. The prestack migration can take many forms. In particular, prestack time migration of common‐angle sections provides a convenient tool for improving the lateral resolution and spatial positioning of AVO anomalies. In this approach, a plane‐wave decomposition is first applied in the offset direction, separating the wavefield into different propagating angles. The data are then gathered into common‐angle sections and migrated one angle at a time. The common‐angle migrations have a simple form and are shown to adequately preserve amplitude as a function of angle. Normal AVO analysis is then applied to the prestack migrated data. Examples using seismic lines from the Gulf of Mexico show how migration improves AVO analysis. In the first set of examples, migration is shown to improve imaging of subtle spatial variations in bright spots. Subsequent AVO analysis reveals dim spots associated with dry‐hole locations that were not resolvable using traditional processing techniques, including both conventional AVO and poststack migration. A second set of examples shows improvements in AVO response after migration is used to reduce interference from coherent noise and diffractions. A final example shows the impact of migration on the spatial location of dipping AVO anomalies. In all cases, migration improves both the signal‐to‐noise ratio and spatial resolution of AVO anomalies.

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35

Ross, Christopher P. "Incomplete AVO near salt structures." GEOPHYSICS 57, no. 4 (April 1992): 543–53. http://dx.doi.org/10.1190/1.1443268.

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Amplitude versus offset (AVO) measurements for deep hydrocarbon‐bearing sands can be compromised when made in close proximity to a shallow salt piercement structure. Anomalous responses are observed, particularly on low acoustic impedance bright spots. CMP data from key seismic profiles traversing the bright spots do not show the expected Class 3 offset responses. On these CMPs, significant decrease of far trace energy is observed. CMP data from other seismic profiles off‐structure do exhibit the Class 3 offset responses, implying that structural complications may be interfering with the offset response. A synthetic AVO gather was generated using well log data, which supports the off‐structure Class 3 responses, further reinforcing the concept of structurally‐biased AVO responses. Acoustic, pseudo‐spectral modeling of the structure substantiates the misleading AVO response. Pseudo‐spectral modeling results suggest that signal degradation observed on the far offsets is caused by wavefield refraction—a shadow zone, where the known hydrocarbon‐bearing sands are not completely illuminated. Such shadow zones obscure the correct AVO response, which may have bearing on exploration and development.

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36

Wandler, Aaron, Brian Evans, and Curtis Link. "AVO as a fluid indicator: A physical modeling study." GEOPHYSICS 72, no. 1 (January 2007): C9—C17. http://dx.doi.org/10.1190/1.2392817.

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Information on time-lapse changes in seismic amplitude variation with offset (AVO) from a reservoir can be used to optimize production. We designed a scaled physical model experiment to study the AVO response of mixtures of brine, oil, and carbon dioxide at pressures of 0, 1.03, and [Formula: see text]. The small changes in density and velocity for each fluid because of increasing pressure were not detectable and were assumed to lie within the error of the experiment. However, AVO analysis was able to detect changes in the elastic properties between fluids that contained oil and those that did not. When the AVO response was plotted in the AVO intercept-gradient domain, fluids containing oil were clearly separated from fluids not containing oil. This was observed in the AVO response from both the top and base of the fluids in the physical model. We then compared the measured AVO response with the theoretical AVO response given by the Zoeppritz equations. The measured and theoretical AVO intercept responses for the top fluid reflection agree well, although the AVO gradients disagree slightly. For the fluid base reflection, the measured and theoretical responses are in close agreement.

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Chen, He, John P. Castagna, Raymon L. Brown, and Antonio C. B. Ramos. "Three‐parameter AVO crossplotting in anisotropic media." GEOPHYSICS 66, no. 5 (September 2001): 1359–63. http://dx.doi.org/10.1190/1.1487081.

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Amplitude versus offset (AVO) interpretation can be facilitated by crossplotting AVO intercept (A), gradient (B), and curvature (C) terms. However, anisotropy, which exists in the real world, usually complicates AVO analysis. Recognizing anisotropic behavior on AVO crossplots can help avoid AVO interpretation errors. Using a modification to a three‐term (A, B, and C) approximation to the exact anisotropic reflection coefficients for transversely isotropic media, we find that anisotropy has a nonlinear effect on an A versus C crossplot yet causes slope changes and differing intercepts on A versus B or C crossplots. Empirical corrections that result in more accurate crossplot interpretation are introduced for specific circumstances.

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Blangy, J. P. "AVO in transversely Isotropic media—An overview." GEOPHYSICS 59, no. 5 (May 1994): 775–81. http://dx.doi.org/10.1190/1.1443635.

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The amplitude variation with offset (AVO) responses of elastic transversely isotropic media are sensitive to contrasts in both of Thomsen’s anisotropic parameters δ and ε. The equation describing P-P reflections indicates that the smaller the contrasts in isotropic properties (compressional velocity, shear velocity, and density) and the larger the contrasts in δ and ε across an interface of reflection, the greater the effects of anisotropy on the AVO signature. Contrasts in δ are most important under small‐to‐medium angles of incidence as previously described by Banik (1987), while contrasts in ε can have a strong influence on amplitudes for the larger angles of incidence commonly encountered in exploration (20 degrees and beyond). Consequently, using Rutherford and Williams’ AVO classification of isotropic gas sands, type I gas sands overlain by a transversely isotropic (TI) shale exhibit a larger decrease in AVO than if the shale had been isotropic, and type III gas sands overlain by a transversely isotropic (TI) shale exhibit a larger increase in AVO than if the shale had been isotropic. Furthermore, it is possible for a “type III” isotropic water sand to exhibit an “unexpected) increase in AVO if the overlying shale is sufficiently anisotropic. More quantitative AVO interpretations in TI media require considerations of viscoelastic TI in addition to elastic TI and lead to complicated integrated earth models. However, when elastic and viscoelastic TI have the same axis of symmetry in a shale overlying an isotropic sand, both elastic and viscoelastic TI affect the overall AVO response in the same direction by constructively increasing/decreasing the isotropic component of the AVO response. Continued efforts in this area will lead to more realistic reservoir models and hopefully answer some of the unexplained pitfalls in AVO interpretation.

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Li, Kun, Xing-Yao Yin, Zhao-Yun Zong, and Hai-Kun Lin. "Seismic AVO statistical inversion incorporating poroelasticity." Petroleum Science 17, no. 5 (July 30, 2020): 1237–58. http://dx.doi.org/10.1007/s12182-020-00483-5.

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Abstract Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

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Hamlyn, Wes. "Thin beds, tuning, and AVO." Leading Edge 33, no. 12 (December 2014): 1394–96. http://dx.doi.org/10.1190/tle33121394.1.

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In this tutorial, we will explore two topics that are particularly relevant to quantitative seismic interpretation — thin-bed tuning and AVO analysis. Specifically, we will examine the impact of thin beds on prestack seismic amplitudes and subsequent effects on AVO attribute values.

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Castagna, John P., Herbert W. Swan, and Douglas J. Foster. "Framework for AVO gradient and intercept interpretation." GEOPHYSICS 63, no. 3 (May 1998): 948–56. http://dx.doi.org/10.1190/1.1444406.

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Amplitude variation with offset (AVO) interpretation may be facilitated by crossplotting the AVO intercept (A) and gradient (B). Under a variety of reasonable petrophysical assumptions, brine‐saturated sandstones and shales follow a well‐defined “background” trend in the A-B plane. Generally, A and B are negatively correlated for “background” rocks, but they may be positively correlated at very high [Formula: see text] ratios, such as may occur in very soft shallow sediments. Thus, even fully brine‐saturated shallow events with large reflection coefficients may exhibit large increases in AVO. Deviations from the background trend may be indicative of hydrocarbons or lithologies with anomalous elastic properties. However, in contrast to the common assumptions that gas‐sand amplitude increases with offset, or that the reflection coefficient becomes more negative with increasing offset, gas sands may exhibit a variety of AVO behaviors. A classification of gas sands based on location in the A-B plane, rather than on normal‐incidence reflection coefficient, is proposed. According to this classification, bright‐spot gas sands fall in quadrant III and have negative AVO intercept and gradient. These sands exhibit the amplitude increase versus offset which has commonly been used as a gas indicator. High‐impedance gas sands fall in quadrant IV and have positive AVO intercept and negative gradient. Consequently, these sands initially exhibit decreasing AVO and may reverse polarity. These behaviors have been previously reported and are addressed adequately by existing classification schemes. However, quadrant II gas sands have negative intercept and positive gradient. Certain “classical” bright spots fall in quadrant II and exhibit decreasing AVO. Examples show that this may occur when the gas‐sand shear‐wave velocity is lower than that of the overlying formation. Common AVO analysis methods such as partial stacks and product (A × B) indicators are complicated by this nonuniform gas‐sand behavior and require prior knowledge of the expected gas‐sand AVO response. However, Smith and Gidlow’s (1987) fluid factor, and related indicators, will theoretically work for gas sands in any quadrant of the A-B plane.

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Williamson, P. E., and F. Kroh. "THE ROLE OF AMPLITUDE VERSUS OFFSET TECHNOLOGY IN PROMOTING OFFSHORE PETROLEUM EXPLORATION IN AUSTRALIA." APPEA Journal 47, no. 1 (2007): 163. http://dx.doi.org/10.1071/aj06009.

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Amplitude versus offset (AVO) technology has proved itself useful in petroleum exploration in various parts of the world, particularly for gas exploration. To determine if modern AVO compliant processing could identify potential anomalies for exploration of open acreage offshore Australia, Geoscience Australia reprocessed parts of four publicly available long cable lines. These lines cover two 2006 acreage release areas on the Exmouth Plateau and in the Browse Basin on the North West Shelf. An earlier study has also been done on two publicly available long cable lines from Geoscience Australia’s Bremer Basin study and cover areas from the 2005 frontier acreage release on the southern margin. The preliminary results from these three reprocessing efforts produced AVO anomalies and were made publicly available to assist companies interested in assessing the acreage. The results of the studies and associated data are available from Geoscience Australia at the cost of transfer.The AVO data from the Exmouth Plateau show AVO anomalies including one that appears to be at the Jurassic level of the reservoir in the Jansz/Io supergiant gas field in adjacent acreage to the north. The AVO data from the Caswell Sub-basin of the Browse Basin show an AVO anomaly at or near the stratigraphic zone of the Brecknock South–1 gas discovery to the north. The geological settings of strata possibly relating to two AVO anomalies in the undrilled Bremer Basin are in the Early Cretaceous section, where lacustrine sandstones are known to occur. The AVO anomalies from the three studies are kilometres in length along the seismic lines.These preliminary results from Geoscience Australiaand other AVO work that has been carried out by industry show promise that AVO compliant processing has value—particularly for gas exploration offshore Australia—and that publicly available long-cable data can be suitable for AVO analysis.

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de Bruin, J. A. "Domains and trends in AVO." First Break 38, no. 2 (February 1, 2020): 29–36. http://dx.doi.org/10.3997/1365-2397.fb2020007.

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Hampson, Dan. "AVO inversion, theory and practice." Leading Edge 10, no. 6 (June 1991): 39–42. http://dx.doi.org/10.1190/1.1436820.

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Foster, D. J., R. G. Keys, and J. M. Reilly. "Another perspective on AVO crossplotting." Leading Edge 16, no. 9 (September 1997): 1233–39. http://dx.doi.org/10.1190/1.1437768.

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Cambois, Guillaume. "CanP-wave AVO be quantitative?" Leading Edge 19, no. 11 (November 2000): 1246–51. http://dx.doi.org/10.1190/1.1438516.

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Simm, Rob, Roy White, and Richard Uden. "The anatomy of AVO crossplots." Leading Edge 19, no. 2 (February 2000): 150–55. http://dx.doi.org/10.1190/1.1438557.

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Roden, Rocky, and Rebecca Latimer. "An introduction—Rock geophysics/AVO." Leading Edge 22, no. 10 (October 2003): 987. http://dx.doi.org/10.1190/1.1623638.

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Li, Yongyi, Jonathan Downton, and Yong Xu. "Practical aspects of AVO modeling." Leading Edge 26, no. 3 (March 2007): 295–311. http://dx.doi.org/10.1190/1.2715053.

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Ho, Wai‐Ching. "Quick screen for AVO anomalies." Leading Edge 8, no. 12 (December 1989): 72–73. http://dx.doi.org/10.1190/1.1439601.

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Journal articles on the topic 'AVO'

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