- Home
- A-Z Publications
- Geophysical Prospecting
- Issue Home
Geophysical Prospecting - Current Issue
Volume 73, Issue 1, 2024
- ISSUE INFORMATION
-
- ORIGINAL ARTICLE
-
-
-
Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks
Authors Ravi Kant, Brijesh Kumar, Ajay P. Singh, G. Hema, S. P. Maurya, Raghav Singh, K. H. Singh and Piyush SarkarAbstractSeismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (>20%) with a lower acoustic impedance of 6000–8500 m/s g cm3, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two‐way travel time, provides high‐resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high‐resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity.
-
-
-
-
Three methods of visco‐acoustic migration based on the De Wolf approximation and comparison of their migration images
AuthorsAbstractThe viscosity of a medium affects the amplitude attenuation and velocity dispersion of seismic waves. Therefore, it is necessary to consider these factors during migration. First, to eliminate the viscous effect of a medium, we combine the Futterman model with the integral equation of the De Wolf approximation to construct a compensation operator of the De Wolf approximation for a visco‐acoustic medium. Next, we use the visco‐acoustic screen approximation method to realize the continuation operator then establish a prestack depth migration algorithm. Finally, an error analysis, impulse response test and model test are performed. The results show that three different generalized visco‐acoustic screen methods (phase screen method, generalized screen method and extended local Born Fourier method) can satisfactorily compensate for the attenuation of deep interface amplitude. Among these methods, the visco‐acoustic extended local Born Fourier method has the highest accuracy and the best compensation effect.
-
-
-
3D Controlled‐source electromagnetic modelling in anisotropic media using secondary potentials and a cascadic multigrid solver
Authors Kejia Pan, Jinxuan Wang, Xu Han, Zhengyong Ren, Weiwei Ling and Rongwen GuoAbstractQuantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc.
-
-
-
Seismic fault detection with sliding windowed differential cepstrum–based coherence analysis
Authors Qi Ran, Kang Chen, Cong Tang, Long Wen, Ming Zeng, Han Liang, Guang‐rong Zhang, Han Xiao and Ya‐juan XueAbstractCepstral decomposition is beneficial for highlighting certain geological features within the particular quefrency bands which may be deeply buried within the wide quefrency range of the seismic data. Converting seismic traces into the corresponding cepstrum components can better analyse some characteristics of underground strata than the traditional spectral decomposition methods. We propose the sliding windowed differential cepstrum–based coherence analysis approach to delineate the fault features. First, the data are decomposed using a sliding windowed differential cepstrum, which results in multi‐cepstrum data of corresponding quefrency of certain bandwidth. These different multi‐cepstrum data may highlight the different stratigraphic features in a certain quefrency band. We select the first‐order common quefrency volume as the featured attribute. Then, eigenstructure‐based coherence is applied on the first‐order common quefrency data volume to statistically obtain the fault detection result with a finer and sharper image. Synthetic data and field data examples show that the proposed method has the ability to better visualize all the possible subtle and minor faults present in the data more accurately and discernibly than the traditional coherence method. Compared with the ant‐tracking method, the proposed method is more effective in revealing the major faults. It is hoped that this work will complement current fault detection methods with the addition of the cepstral‐based method.
-
Volumes & issues
-
Volume 73 (2024)
-
Volume 72 (2023 - 2024)
-
Volume 71 (2022 - 2023)
-
Volume 70 (2021 - 2022)
-
Volume 69 (2021)
-
Volume 68 (2020)
-
Volume 67 (2019)
-
Volume 66 (2018)
-
Volume 65 (2017)
-
Volume 64 (2015 - 2016)
-
Volume 63 (2015)
-
Volume 62 (2014)
-
Volume 61 (2013)
-
Volume 60 (2012)
-
Volume 59 (2011)
-
Volume 58 (2010)
-
Volume 57 (2009)
-
Volume 56 (2008)
-
Volume 55 (2007)
-
Volume 54 (2006)
-
Volume 53 (2005)
-
Volume 52 (2004)
-
Volume 51 (2003)
-
Volume 50 (2002)
-
Volume 49 (2001)
-
Volume 48 (2000)
-
Volume 47 (1999)
-
Volume 46 (1998)
-
Volume 45 (1997)
-
Volume 44 (1996)
-
Volume 43 (1995)
-
Volume 42 (1994)
-
Volume 41 (1993)
-
Volume 40 (1992)
-
Volume 39 (1991)
-
Volume 38 (1990)
-
Volume 37 (1989)
-
Volume 36 (1988)
-
Volume 35 (1987)
-
Volume 34 (1986)
-
Volume 33 (1985)
-
Volume 32 (1984)
-
Volume 31 (1983)
-
Volume 30 (1982)
-
Volume 29 (1981)
-
Volume 28 (1980)
-
Volume 27 (1979)
-
Volume 26 (1978)
-
Volume 25 (1977)
-
Volume 24 (1976)
-
Volume 23 (1975)
-
Volume 22 (1974)
-
Volume 21 (1973)
-
Volume 20 (1972)
-
Volume 19 (1971)
-
Volume 18 (1970)
-
Volume 17 (1969)
-
Volume 16 (1968)
-
Volume 15 (1967)
-
Volume 14 (1966)
-
Volume 13 (1965)
-
Volume 12 (1964)
-
Volume 11 (1963)
-
Volume 10 (1962)
-
Volume 9 (1961)
-
Volume 8 (1960)
-
Volume 7 (1959)
-
Volume 6 (1958)
-
Volume 5 (1957)
-
Volume 4 (1956)
-
Volume 3 (1955)
-
Volume 2 (1954)
-
Volume 1 (1953)