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Journal articles on the topic 'AVO inversion'

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Published: 4 June 2021

Last updated: 9 February 2022

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1

Lu, Jun, Yun Wang, Jingyi Chen, and Ying An. "Joint anisotropic amplitude variation with offset inversion of PP and PS seismic data." GEOPHYSICS 83, no. 2 (2018): N31—N50. http://dx.doi.org/10.1190/geo2016-0516.1.

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With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solutio

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2

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

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3

Buland, Arild, and Henning Omre. "Bayesian linearized AVO inversion." GEOPHYSICS 68, no. 1 (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;

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4

Simmons, James L., and Milo M. Backus. "Waveform‐based AVO inversion and AVO prediction‐error." GEOPHYSICS 61, no. 6 (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 perturb

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5

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

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6

Sengupta, Madhumita, Houzhu Zhang, Yang Zhao, Mike Jervis, and Dario Grana. "Direct depth-domain Bayesian amplitude-variation-with-offset inversion." GEOPHYSICS 86, no. 5 (2021): M167—M176. http://dx.doi.org/10.1190/geo2020-0219.1.

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We have developed a new approach to perform Bayesian linearized amplitude-variation-with-offset (AVO) inversion in the depth domain using nonstationary wavelets. Bayesian linearized AVO inversion, a hybrid approach combining physics-based models with statistical learning, has gained immense popularity because of its superior computational speed and ability to estimate uncertainties in inverted model parameters. Bayesian linearized AVO inversion is performed on time-domain seismic data; therefore, depth-imaged seismic cannot be inverted directly using this method and would require depth-to-time

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7

Beretta, Matteo Mario, Giancarlo Bernasconi, and Giuseppe Drufuca. "AVO and AVA inversion for fractured reservoir characterization." GEOPHYSICS 67, no. 1 (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 tomo

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8

Townend, Edward, and Michael Kemper. "Introduction to this special section: AVO inversion." Leading Edge 38, no. 10 (2019): 752–53. http://dx.doi.org/10.1190/tle38100752.1.

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It has been more than three years since The Leading Edge last published a special section on amplitude variation with offset (AVO) inversion, and interest in the subject remains strong. This past spring, SEG hosted a joint symposium in Houston, Texas, on the “Resurgence of seismic inversion,” and the body of talks and case studies demonstrated the method's continued relevance to making impactful drilling decisions. Despite this, and despite AVO inversion's position as a mature and well-established technique, there are an abundance of examples in which inaccurate AVO predictions have led to dra

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9

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

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10

Lang, Xiaozheng, and Dario Grana. "Bayesian linearized petrophysical AVO inversion." GEOPHYSICS 83, no. 3 (2018): M1—M13. http://dx.doi.org/10.1190/geo2017-0364.1.

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Seismic reservoir characterization aims to provide a 3D model of rock and fluid properties based on measured seismic data. Petrophysical properties, such as porosity, mineral volumes, and water saturation, are related to elastic properties, such as velocity and impedance, through a rock-physics model. Elastic attributes can be obtained from seismic data through seismic modeling. Estimation of the properties of interest is an inverse problem; however, if the forward model is nonlinear, computationally demanding inversion algorithms should be adopted. We have developed a linearized forward model

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11

Buland, A., and M. Landrø. "AVO inversion, theory and applications." Journal of Applied Geophysics 34, no. 2 (1995): 140. http://dx.doi.org/10.1016/0926-9851(96)80869-x.

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12

Ursenbach, Charles P., and Robert R. Stewart. "Two-term AVO inversion: Equivalences and new methods." GEOPHYSICS 73, no. 6 (2008): C31—C38. http://dx.doi.org/10.1190/1.2978388.

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Most amplitude-variation-with-offset (AVO) studies use two-parameter inversion methods that are approximations of a more general three-parameter method based on the Aki-Richards approximation. Two-parameter methods are popular because the three-parameter inversion is often plagued by numerical instability. Reducing the dimensionality of the parameter space stabilizes the inversion. A variety of constraints can accomplish this, and these lead to the multiplicity of current two-parameter methods. It would be useful to understand relationships between various two-parameter methods. To this end, w

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13

Li, Kun, Xing-Yao Yin, Zhao-Yun Zong, and Hai-Kun Lin. "Seismic AVO statistical inversion incorporating poroelasticity." Petroleum Science 17, no. 5 (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 mod

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14

Contreras, Arturo, Andre Gerhardt, Paul Spaans, and Matthew Docherty. "Characterization of fluvio-deltaic gas reservoirs through AVA deterministic, stochastic, and wave-equation-based seismic inversion: A case study from the Carnarvon Basin, Western Australia." Leading Edge 39, no. 2 (2020): 92–101. http://dx.doi.org/10.1190/tle39020092.1.

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Multiple state-of-the-art inversion methods have been implemented to integrate 3D seismic amplitude data, well logs, geologic information, and spatial variability to produce models of the subsurface. Amplitude variation with angle (AVA) deterministic, stochastic, and wave-equation-based amplitude variation with offset (WEB-AVO) inversion algorithms are used to describe Intra-Triassic Mungaroo gas reservoirs located in the Carnarvon Basin, Western Australia. The interpretation of inverted elastic properties in terms of lithology- and fluid-sensitive attributes from AVA deterministic inversion p

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15

Skopintseva, Lyubov, and Alexey Stovas. "Overburden dependent AVA inversion." GEOPHYSICS 75, no. 2 (2010): C15—C23. http://dx.doi.org/10.1190/1.3332529.

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Amplitude-variation-with-offset (AVO) analysis is strongly dependent on interpretation of the estimated traveltime parameters. In practice, we can estimate two or three traveltime parameters that require interpretation within the families of two- or three-parameter velocity models, respectively. Increasing the number of model parameters improves the quality of overburden description and reduces errors in AVO analysis. We have analyzed the effect of two- and three-parameter velocity model interpretation for the overburden on AVO data and have developed error estimates in the reservoir parameter

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16

Du, Qizhen, Bo Zhang, Xianjun Meng, Chengfeng Guo, Gang Chen, and Guodong Huo. "Two-step joint PP- and PS-wave three-term amplitude-variation with offset inversion." Interpretation 4, no. 4 (2016): T613—T625. http://dx.doi.org/10.1190/int-2016-0056.1.

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Three-term amplitude-variation with offset (AVO) inversion generally suffers from instability when there is limited prior geologic or petrophysical constraints. Two-term AVO inversion shows higher instability compared with three-term AVO inversion. However, density, which is important in the fluid-type estimation, cannot be recovered from two-term AVO inversion. To reliably predict the P- and S-waves and density, we have developed a robust two-step joint PP- and PS-wave three-term AVO-inversion method. Our inversion workflow consists of two steps. The first step is to estimate the P- and S-wav

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17

Mallick, Subhashis, and Samar Adhikari. "Amplitude-variation-with-offset and prestack-waveform inversion: A direct comparison using a real data example from the Rock Springs Uplift, Wyoming, USA." GEOPHYSICS 80, no. 2 (2015): B45—B59. http://dx.doi.org/10.1190/geo2014-0233.1.

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Recent advances in seismic data acquisition and processing allow routine extraction of offset-/angle-dependent reflection amplitudes from prestack seismic data for quantifying subsurface lithologic and fluid properties. Amplitude-variation-with-offset (AVO) inversion is the most commonly used practice for such quantification. Although quite successful, AVO has a few shortcomings primarily due to the simplicity in its inherent assumptions, and for any quantitative estimation of reservoir properties, they are generally interpreted in combination with other information. In recent years, waveform-

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18

PENG, Zhen-Ming, Ya-Lin LI, Wen-Ge WEI, Zhen-Hua HE, and Da-Jun LI. "Nonlinear AVO Inversion Using Particle Filter." Chinese Journal of Geophysics 51, no. 4 (2008): 862–71. http://dx.doi.org/10.1002/cjg2.1280.

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19

Buland, Arild, Martin Landrø, Mona Andersen, and Terje Dahl. "AVO inversion of Troll Field data." GEOPHYSICS 61, no. 6 (1996): 1589–602. http://dx.doi.org/10.1190/1.1444078.

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A stratigraphic elastic inversion scheme has been applied to a data set from the Troll East Field, offshore Norway. The objective of the present work is to obtain estimates of the P‐ and S‐wave velocities and densities of the subsurface. The inversion is carried out on τ − p transformed common depth‐point (CMP) gathers. The forward modeling is performed by convolving a wavelet with the reflectivity that includes water‐bottom multiples, transmission effects, and absorption and array effects. A damped Gauss‐Newton algorithm is used to minimize a least‐squares misfit function. Inversion results s

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20

Asaka, Michinori. "AVO inversion using pseudoisotropic elastic properties." Leading Edge 40, no. 1 (2021): 52–59. http://dx.doi.org/10.1190/tle40010052.1.

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Amplitude variation with offset (AVO) inversion of an anisotropic data set is a challenging task. Nonnegligible differences in the anisotropy parameters between the various lithologies make the seismic data AVO response completely different from the isotropic synthetic seismogram. In this case, it is difficult to invert for VP/VS and density consistent with well-log data. AVO inversion using pseudoisotropic elastic properties is a practical solution to this problem. Verification of this method was performed using data from an offshore Western Australia field. It was found that wavelet extracti

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21

Kolbj⊘rnsen, Odd, Andreas Kjelsrud Evensen, Espen Harris Nilsen, and Jan Erik Lie. "Digital superresolution in seismic AVO inversion." Leading Edge 38, no. 10 (2019): 791–99. http://dx.doi.org/10.1190/tle38100791.1.

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The sparseness promoted by the total variation norm is utilized to achieve superresolution amplitude-variation-with-offset (AVO) inversion. The total variation norm promotes solutions that have constant values within unspecified regions and thus are well suited for an earth model consisting of layers bounded by faults and erosion surfaces. Algorithmic developments from digital image and video restoration are utilized to solve the geophysical problem. A spatial point spread function is used to model the resulting effect of wave propagation, migration, and processing. The methodology is compared

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22

Wang, Lingqian, Hui Zhou, Bo Yu, Yanxin Zhou, Wenling Liu, and Yukun Tian. "Inversion for Geofluid Discrimination Based on Poroelasticity and AVO Inversion." Geofluids 2019 (November 26, 2019): 1–17. http://dx.doi.org/10.1155/2019/2656747.

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Geofluid discrimination plays an important role in reservoir characterization and prospect identification. Compared with other fluid indicators, the effective pore-fluid bulk modulus is more sensitive to the property of fluid contained in reservoirs. We combine the empirical relations with deterministic models to form a new kind of linearized relationship between the mixed fluid/rock term and the fluid modulus. On the one hand, the linearized relationship can decouple the fluid bulk modulus from the mixed fluid/rock term; on the other hand, the decoupled terms are more stable especially in low

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23

Liu, Jinyue, and Yanghua Wang. "Seismic simultaneous inversion using a multidamped subspace method." GEOPHYSICS 85, no. 1 (2019): R1—R10. http://dx.doi.org/10.1190/geo2018-0470.1.

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Seismic inversion of amplitude variation with offset (AVO) plays a key role in seismic interpretation and reservoir characterization. The AVO inversion should be a simultaneous inversion that inverts for three elastic parameters simultaneously: the P-wave impedance, S-wave impedance, and density. Using only seismic P-wave reflection data with a limited source-receiver offset range, the AVO simultaneous inversion can obtain two elastic parameters reliably, but it is difficult to invert for the third parameter, usually the density term. To address this difficulty in the AVO simultaneous inversio

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24

Purnamasari, Anastasia Neni Candra. "Metode Inversi AVO Simultan untuk Mengetahui Sebaran Hidrokarbon Formasi Baturaja, Lapangan "Wine”, Cekungan Sumatra Selatan." Jurnal Offshore: Oil, Production Facilities and Renewable Energy 1, no. 1 (2017): 26. http://dx.doi.org/10.30588/jo.v1i1.239.

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<p>Data seismik 3D (<em>CDP</em> <em>gather</em>) pada daerah penelitian dilakukan proses inversi prestack yaitu inversi AVO simultan untuk mengetahui sebaran hidrokarbon. Data seismik 3D terbentang dengan jangkauan <em>inline</em> 1003-1302 dan <em>xline</em> 5002-5300. Metode inversi AVO simultan dilakukan dengan data masukan berupa <em>angle stack</em> yang diinversi secara bersama-sama (simultan) untuk menghasilkan impedansi-P, impedansi-S dan densitas. Dari hasil inversi impedansi-P dan inversi impedansi-S didapatkan nilai

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25

Liu, Hongxing, Jingye Li, Xiaohong Chen, Bo Hou, and Li Chen. "Amplitude variation with offset inversion using the reflectivity method." GEOPHYSICS 81, no. 4 (2016): R185—R195. http://dx.doi.org/10.1190/geo2015-0332.1.

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Most existing amplitude variation with offset (AVO) inversion methods are based on the Zoeppritz’s equation or its approximations. These methods assume that the amplitude of seismic data depends only on the reflection coefficients, which means that the wave-propagation effects, such as geometric spreading, attenuation, transmission loss, and multiples, have been fully corrected or attenuated before inversion. However, these requirements are very strict and can hardly be satisfied. Under a 1D assumption, reflectivity-method-based inversions are able to handle transmission losses and internal mu

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26

Alemie, Wubshet, and Mauricio D. Sacchi. "High-resolution three-term AVO inversion by means of a Trivariate Cauchy probability distribution." GEOPHYSICS 76, no. 3 (2011): R43—R55. http://dx.doi.org/10.1190/1.3554627.

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Three-term AVO inversion can be used to estimate P-wave velocity, S-wave velocity, and density perturbations from reflection seismic data. The density term, however, exhibits little sensitivity to amplitudes and, therefore, its inversion is unstable. One way to stabilize the density term is by including a scale matrix that provides correlation information between the three unknown AVO parameters. We investigate a Bayesian procedure to include sparsity and a scale matrix in the three-term AVO inversion problem. To this end, we model the prior distribution of the AVO parameters via a Trivariate

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27

Chen, Marc-André P., Michael Riedel, Roy D. Hyndman, and Stan E. Dosso. "AVO inversion of BSRs in marine gas hydrate studies." GEOPHYSICS 72, no. 2 (2007): C31—C43. http://dx.doi.org/10.1190/1.2435604.

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We examine the usefulness of amplitude versus offset (AVO) analysis of bottom-simulating reflections (BSRs) for estimating associated marine gas hydrate and free-gas concentrations. A nonlinear Bayesian inversion is applied to estimate marginal probability distributions (MPDs) of physical parameters at a BSR interface, which are related to overlying gas hydrate and underlying free-gas concentrations via rock physics modeling. The problem is constrained further by prior information and re-parameterization of inversion results. Inversion of BSR AVO data from offshore Vancouver Island, Canada, sh

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28

Stovas, Alexey, Martin Landrø, and Per Avseth. "AVO attribute inversion for finely layered reservoirs." GEOPHYSICS 71, no. 3 (2006): C25—C36. http://dx.doi.org/10.1190/1.2197487.

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Assuming that a turbidite reservoir can be approximated by a stack of thin shale-sand layers, we use standard amplitude variaiton with offset (AVO) attributes to estimate net-to-gross (N/G) and oil saturation. Necessary input is Gassmann rock-physics properties for sand and shale, as well as the fluid properties for hydrocarbons. Required seismic input is AVO intercept and gradient. The method is based upon thin-layer reflectivity modeling. It is shown that random variability in thickness and seismic properties of the thin sand and shale layers does not change significantly the AVO attributes

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29

Shou, Hao, Hong Liu, and Jianhu Gao. "AVO inversion based on common shot migration." Applied Geophysics 3, no. 2 (2006): 98–104. http://dx.doi.org/10.1007/s11770-006-0015-2.

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30

Ball, Vaughn, Mosab Nasser, and Odd Kolbj⊘rnsen. "Introduction to this special section: AVO inversion." Leading Edge 35, no. 5 (2016): 399–404. http://dx.doi.org/10.1190/tle35050399.1.

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31

Liu, Yang, Jiashu Zhang, and Guangmin Hu. "Iterative reweighted least M-estimate AVO inversion." Exploration Geophysics 46, no. 2 (2015): 159–67. http://dx.doi.org/10.1071/eg13038.

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32

Li, Chao, Jinmiao Zhang, and Zhenyu Zhu. "Bayesian AVO inversion with consistent angle parameters." Journal of Applied Geophysics 139 (April 2017): 246–56. http://dx.doi.org/10.1016/j.jappgeo.2017.02.020.

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33

Liu, Hanmin, Xuesong Yan, and Qinghua Wu. "An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion." Symmetry 11, no. 10 (2019): 1291. http://dx.doi.org/10.3390/sym11101291.

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Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search pro

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34

Cheng, Guangsen, Xingyao Yin, and Zhaoyun Zong. "Frequency-dependent spherical-wave nonlinear AVO inversion in elastic media." Geophysical Journal International 223, no. 2 (2020): 765–76. http://dx.doi.org/10.1093/gji/ggaa312.

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SUMMARY The plane-wave reflection coefficient (PRC) plays a remarkable role in conventional amplitude variation with offset (AVO) analysis and inversion. Compared with the widely exploited PRC that breaks down at the near- and supercritical incidence angles, the spherical-wave reflection coefficient (SRC) can overcome the influence of wide-angle reflection and give an accurate description of the actual seismic wave reflection phenomenon based on spherical-wave fronts. However, SRC is not widely used in AVO inversion due to its nonlinearity and computational complexity. In our study, the charac

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Liu, Jiong, Jun-rui Ning, Xi-wu Liu, Chun-yuan Liu, and Tian-sheng Chen. "An Improved Scheme of Frequency-Dependent AVO Inversion Method and Its Application for Tight Gas Reservoirs." Geofluids 2019 (February 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/3525818.

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AVO inversion is a seismic exploration methodology used to predict the earth’s elastic parameters and thus rocks and fluid properties. It is built up on elastic theory and does not consider the seismic dispersion in real strata. Recent experiments and theory of rock physics have shown that in hydrocarbon-bearing rocks, especially in gas-bearing ones, the change of seismic velocity with frequency may be pretty remarkable for fluid flow in pore space. Some scholars proposed methods about seismic dispersion, such as frequency-dependent AVO inversion, to forecast oil and gas reservoirs underground

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36

Skopintseva, Lyubov, Milana Ayzenberg, Martin Landrø, Tatyana Nefedkina, and Arkady M. Aizenberg. "Long-offset AVO inversion of PP reflections from plane interfaces using effective reflection coefficients." GEOPHYSICS 76, no. 6 (2011): C65—C79. http://dx.doi.org/10.1190/geo2010-0079.1.

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A conventional amplitude variation with offset (AVO) inversion is based on geometrical seismics which exploit plane-wave reflection coefficients to describe the reflection phenomenon. Widely exploited linearizations of plane-wave coefficients are mostly valid at pre-critical offsets for media with almost flat and weak-contrast interfaces. Existing linearizations do not account for the seismic frequency range by ignoring the frequency content of the wavelet, which is a strong assumption. Plane-wave reflection coefficients do not fully describe the reflection of seismic waves at near-critical an

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37

Zhang, Fanchang, Jingyang Yang, Chuanhui Li, Dong Li, and Yang Gao. "Direct inversion for reservoir parameters from prestack seismic data." Journal of Geophysics and Engineering 17, no. 6 (2020): 993–1004. http://dx.doi.org/10.1093/jge/gxaa058.

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Abstract Reliably estimating reservoir parameters is the final target in reservoir characterisation. Conventionally, estimating reservoir characters from seismic inversion is implemented by indirect approaches. The indirect estimation of reservoir parameters from inverted elastic parameters, however, will produce large bias due to the propagation of errors in the procedure of inversion. Therefore, directly obtaining reservoir parameters from prestack seismic data through a rock-physical model and prestack amplitude variation with offset (AVO) inversion is proposed. A generalised AVO equation i

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Buland, Arild, Odd Kolbjørnsen, and Henning Omre. "Rapid spatially coupled AVO inversion in the Fourier domain." GEOPHYSICS 68, no. 3 (2003): 824–36. http://dx.doi.org/10.1190/1.1581035.

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Spatial coupling of the model parameters in an inversion problem provides lateral consistency and robust solutions. We have defined the inversion problem in a Bayesian framework, where the solution is represented by a posterior distribution obtained from a prior distribution and a likelihood model for the recorded data. The spatial coupling of the model parameters is imposed via the prior distribution by a spatial correlation function. In the Fourier domain, the spatially correlated model parameters can be decoupled, and the inversion problem can be solved independently for each frequency comp

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39

Chen, Huaizhen, Junxiao Li, and Kristopher A. Innanen. "Nonlinear inversion of seismic amplitude variation with offset for an effective stress parameter." GEOPHYSICS 85, no. 4 (2020): R299—R311. http://dx.doi.org/10.1190/geo2019-0154.1.

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Effective stress estimates play important roles in reservoir characterization, for instance, in guiding the selection of fracturing areas in unconventional reservoirs. Based on Gassmann’s fluid substitution model, we have set up a workflow for nonlinear inversion of seismic data for dry rock moduli, fluid factors, and a stress-sensitive parameter. We first make an approximation within the fluid substitution equation, replacing the porosity term with a stress-sensitive parameter. We then derive a linearized reflection coefficient as a function of a stress-parameter reflectivity and reexpress it

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40

Jin, Side, G. Cambois, and C. Vuillermoz. "Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea." GEOPHYSICS 65, no. 5 (2000): 1446–54. http://dx.doi.org/10.1190/1.1444833.

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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 inves

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Zong, Zhaoyun, Kun Li, Xingyao Yin, et al. "Broadband seismic amplitude variation with offset inversion." GEOPHYSICS 82, no. 3 (2017): M43—M53. http://dx.doi.org/10.1190/geo2016-0306.1.

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Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency compon

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42

Madsen, Rasmus Bødker, and Thomas Mejer Hansen. "Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion." GEOPHYSICS 83, no. 2 (2018): N15—N30. http://dx.doi.org/10.1190/geo2017-0404.1.

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A linearized form of Zoeppritz equations combined with the convolution model is widely used in inversion of amplitude variation with offset (AVO) seismic data. This is shown to introduce a “modeling error,” compared with using the full Zoeppritz equations, whose magnitude depends on the degree of subsurface heterogeneity. Then, we evaluate a methodology for quantifying this modeling error through a probability distribution. First, a sample of the unknown probability density describing the modeling error is generated. Then, we determine how this sample can be described by a correlated Gaussian

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Downton, Jonathan E., and Charles Ursenbach. "Linearized amplitude variation with offset (AVO) inversion with supercritical angles." GEOPHYSICS 71, no. 5 (2006): E49—E55. http://dx.doi.org/10.1190/1.2227617.

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Contrary to popular belief, a linearized approximation of the Zoeppritz equations may be used to estimate the reflection coefficient for angles of incidence up to and beyond the critical angle. These supercritical reflection coefficients are complex, implying a phase variation with offset in addition to amplitude variation with offset (AVO). This linearized approximation is then used as the basis for an AVO waveform inversion. By incorporating this new approximation, wider offset and angle data may be incorporated in the AVO inversion, helping to stabilize the problem and leading to more accur

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Cheng, Guangsen, Xingyao Yin, and Zhaoyun Zong. "Nonlinear amplitude-variation-with-offset inversion for Lamé parameters using a direct inversion method." Interpretation 5, no. 3 (2017): SL57—SL67. http://dx.doi.org/10.1190/int-2016-0168.1.

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Prestack seismic inversion is widely used in fluid indication and reservoir prediction. Compared with linear inversion, nonlinear inversion is more precise and can be applied to high-contrast situations. The inversion results can be affected by the parameters’ sensitivity, so the parameterization of nonlinear equations is very significant. Considering the poor nonlinear amplitude-variation-with-offset (AVO) inversion results of impedance and velocity parameters, we adjust the parameters of the nonlinear equation, avoid the inaccuracy caused by parameters sensitivity and get the ideal nonlinear

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Tura, Ali, Christian Hanitzsch, and Henri Calandra. "3-D AVO migration/inversion of field data." Leading Edge 17, no. 11 (1998): 1578. http://dx.doi.org/10.1190/1.1437898.

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Kelly, Mike, Chuck Skidmore, and Dave Ford. "AVO inversion, Part 1: Isolating rock property contrasts." Leading Edge 20, no. 3 (2001): 320–23. http://dx.doi.org/10.1190/1.1438940.

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Skidmore, Chuck, Mike Kelly, and Ray Cotton. "AVO inversion, Part 2: Isolating rock property contrasts." Leading Edge 20, no. 4 (2001): 425–28. http://dx.doi.org/10.1190/1.1438966.

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Xing-Yao, YIN, DENG Wei, and ZONG Zhao-Yun. "AVO INVERSION WITH THE INVERSE OPERATOR ESTIMATION ALGORITHM." Chinese Journal of Geophysics 59, no. 3 (2016): 301–12. http://dx.doi.org/10.1002/cjg2.20235.

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Bailey, Brad, Frazer Barclay, Robert Nesbit, and Andrea Paxton. "Prospect Identification using AVO Inversion and Lithology Prediction." ASEG Extended Abstracts 2010, no. 1 (2010): 1–4. http://dx.doi.org/10.1081/22020586.2010.12041850.

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Cho, David, Klaus Bolding Rasmussen, and Evan Mutual. "Azimuthal AVO inversion of an elliptic orthorhombic medium." Leading Edge 36, no. 10 (2017): 852–57. http://dx.doi.org/10.1190/tle36100852.1.

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