scholarly journals Hydrocarbon Mapping on Reservoir Carbonate Using AVO Inversion Method

2021 ◽  
Vol 6 (1) ◽  
pp. 19-25
Author(s):  
Dendy Setyawan ◽  
Keyword(s):  
Inversion Method ◽  
Target Zones ◽  
Simultaneous Inversion ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
Best Parameter ◽  
Value Changes ◽  
Inversion Analysis ◽  
Angle Gather ◽  
Amplitude Versus

Amplitude Versus Offset (AVO) inversion has been applied for reservoir analysis focused on the horizon carbonate Peutu and Belumai. Simultaneous inversion analysis is used to determine gas anomaly inside carbonate-rocks and it’s spread laterally around target zones. It is based on the fact that small Vpand Vs value changes are going to show the better anomaly to identify reservoir fluid content. The AVO inversion method applies angle gather data as the input and then it is inverted to produce P impedance (Zp) and S impedance (Zs). Zp and Zs are derived to produce Lambda-Rho and Mu-Rho that are sensitive to fluid and lithology. Value of Mu-Rho between 44–65 Gpa gr/cc while value of Lambda-Rho smaller than 10 Gpa gr/cc (for carbonate-rock filled by fluid). This research found that Lambda-Rho is the best parameter to show the existence of hydrocarbon in the case of gas. While Mu-Rho is the best parameter to show the differences in lithology.

scholarly journals Analisis AVO untuk Mengetahui Penyebaran Hidrokarbon Berdasarkan Faktor Fluida (Studi Kasus Lapangan “H” Formasi Talang Akar Cekungan Jawa Barat Utara)

2016 ◽  
Vol 3 (02) ◽  
pp. 209
Author(s):  
Muhammad Nur Handoyo ◽  
Agus Setyawan ◽  
Mualimin Muhammad
Keyword(s):  
Seismic Data ◽  
Class Iii ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
Attribute Analysis ◽  
Fluid Factor ◽  
Inversion Analysis ◽  
Amplitude Versus

<span>Amplitude versus offset (AVO) inversion analysis can be used to determine the spread of <span>hydrocarbons on seismic data. In this study we conducted AVO on reservoir layer Talang <span>Akar’s formation (TAF). AVO inversion results are angle stack, normal incident reflectivity <span>(intercept), gradient and fluid factor. Angle stack attribute analysis showed an AVO anomaly <span>in the reservoir TAF layer, amplitude has increased negative value from near angle stack to far <span>angle stack. The result of crossplot normal incident reflectivity (intercept) with gradient <span>indicates reservoir TAF layer including Class III AVO anomaly. While the analysis of fluid <span>factor attribute has a negative value thus reservoir TAF layer indicates a potential <span>hydrocarbon.</span></span></span></span></span></span></span></span><br /></span>

4D time shift and amplitude versus offset joint (AVO) inversion

2017 ◽  
Author(s):  
Xin Zhan ◽  
Dez Chu ◽  
Brent Wheelock ◽  
David Johnston ◽  
Kaushik Bandyopadhyay ◽  
...  
Keyword(s):  
Time Shift ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
Amplitude Versus

scholarly journals A Born‐WKBJ inversion method for acoustic reflection data

Geophysics ◽  
1981 ◽  
Vol 46 (11) ◽  
pp. 1559-1567 ◽  
Author(s):  
Robert W. Clayton ◽  
Robert H. Stolt
Keyword(s):  
Spatial Frequency ◽  
Bulk Modulus ◽  
High Spatial Frequency ◽  
Inversion Method ◽  
Frequency Variation ◽  
Direct Wave ◽  
Acoustic Reflection ◽  
Reflection Data ◽  
Amplitude Versus Offset ◽  
Amplitude Versus

Density and bulk modulus variations in an acoustic earth are separately recoverable from standard reflection surveys by utililizing the amplitude‐versus‐offset information present in the observed wave fields. Both earth structure and a variable background velocity can be accounted for by combining the Born and WKBJ approximations, in a “before stack” migration with two output sections, one for density variations and the other for bulk modulus variations. For the inversion, the medium is considered to be composed of a known low‐spatial frequency variation (the background) plus an unknown high‐spatial frequency variation in bulk modulus and density (the reflectivity). The division between the background and the reflectivity depends upon the frequency content of the source. For constant background parameters, computations are done in the Fourier domain, where the first part of the algorithm includes a frequency shift identical to that in an F-K migration. The modulus and density variations are then determined by observing in a least‐squares sense amplitude versus offset wavenumber. For a spatially variable background, WKBJ Green’s operators that model the direct wave in a medium with a smoothly varying background are used. A downward continuation with these operators removes the effects of variable velocity from the problem, and, consequently, the remainder of the inversion essentially proceeds as if the background were constant. If the background is strictly depth dependent, the inversion can be expressed in closed form. The method neglects multiples and surface waves and it is restricted to precritical reflections. Density is distinguishable from bulk modulus only if a sufficient range of precritical incident angles is present in the data.

Incomplete AVO near salt structures

Geophysics ◽  
1992 ◽  
Vol 57 (4) ◽  
pp. 543-553 ◽  
Author(s):  
Christopher P. Ross
Keyword(s):  
Seismic Profiles ◽  
Spectral Modeling ◽  
Signal Degradation ◽  
Amplitude Versus Offset ◽  
Bright Spots ◽  
Close Proximity ◽  
Pseudo Spectral ◽  
Amplitude Versus ◽  
Structure Class ◽  
Made In

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.

A direct nonlinear AVO inversion method based on Lamé parameters

2017 ◽  
Author(s):  
Cheng Guangsen ◽  
Xingyao Yin ◽  
Zhaoyun Zong
Keyword(s):  
Inversion Method ◽  
Avo Inversion

scholarly journals Crosswell Seismic Amplitude-Versus-Offset for Detailed Imaging of Facies and Fluid Distribution within Carbonate Oil Reservoirs

2008 ◽  
Author(s):  
Wayne Pennington ◽  
Mohamed Ibrahim ◽  
Roger Turpening ◽  
Sean Trisch ◽  
Josh Richardson ◽  
...  
Keyword(s):  
Oil Reservoirs ◽  
Fluid Distribution ◽  
Seismic Amplitude ◽  
Amplitude Versus Offset ◽  
Detailed Imaging ◽  
Amplitude Versus

scholarly journals QVOA Techniques for Estimation of Fracture Directions

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Vladimir Sabinin
Keyword(s):  
Quality Factor ◽  
Symmetry Axis ◽  
Factor Versus ◽  
Synthetic Data ◽  
Computational Techniques ◽  
High Quality ◽  
Amplitude Versus Offset ◽  
Target Layer ◽  
Analysis Quality ◽  
Amplitude Versus

Some new computational techniques are suggested for estimating symmetry axis azimuth of fractures in the viscoelastic anisotropic target layer in the framework of QVOA analysis (Quality factor Versus Offset and Azimuth). The different QVOA techniques are compared using synthetic viscoelastic surface reflected data with and without noise. I calculated errors for these techniques which depend on different sets of azimuths and intervals of offsets. Superiority of the high-order “enhanced general” and “cubic” techniques is shown. The high-quality QVOA techniques are compared with one of the high-quality AVOA techniques (Amplitude Versus Offset and Azimuth) in the synthetic data with noise and attenuation. Results are comparable.

Simultaneous inversion of Q and reflectivity using dictionary learning

Geophysics ◽  
2021 ◽  
pp. 1-69
Author(s):  
Jie Shao ◽  
Yibo Wang
Keyword(s):  
Conventional Method ◽  
Dictionary Learning ◽  
Computational Efficiency ◽  
Synthetic Data ◽  
Inversion Method ◽  
Learning Method ◽  
Q Value ◽  
Value Set ◽  
Simultaneous Inversion ◽  
Convolution Model

Quality factor ( Q) and reflectivity are two important subsurface properties in seismic data processing and interpretation. They can be calculated simultaneously from a seismic trace corresponding to an anelastic layered model by a simultaneous inversion method based on the nonstationary convolution model. However, the conventional simultaneous inversion method calculates the optimum Q and reflectivity based on the minimum of the reflectivity sparsity by sweeping each Q value within a predefined range. As a result, the accuracy and computational efficiency of the conventional method depend heavily on the predefined Q value set. To improve the performance of the conventional simultaneous inversion method, we have developed a dictionary learning-based simultaneous inversion of Q and reflectivity. The parametric dictionary learning method is used to update the initial predefined Q value set automatically. The optimum Q and reflectivity are calculated from the updated Q value set based on minimizing not only the sparsity of the reflectivity but also the data residual. Synthetic data and two field data sets were used to test the effectiveness of our method. The results demonstrated that our method can effectively improve the accuracy of these two parameters compared to the conventional simultaneous inversion method. In addition, the dictionary learning method can improve computational efficiency up to approximately seven times when compared to the conventional method with a large predefined dictionary.

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