Abstract
Post-stack seismic inversion utilizes post-stack seismic data along with well log data to estimate acoustic impedance. Post-stack seismic inversion is very fast compared to other pre-stack seismic inversion methods and provides a high-resolution subsurface image. This chapter discusses several types of post-stack seismic inversion methods namely model-based inversion, colored inversion, sparse spike inversion, and band-limited inversion. The chapter also includes the synthetic as well as real data examples of above seismic inversion methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ansari HR (2014) Use seismic colored inversion and power-law committee machines based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir. J Appl Geophys 108:61–68
Barrodale I, Roberts FD (1973) An improved algorithm for discrete l-1 linear approximation. SIAM J Numer Anal 10(5):839–848
Barrodale I, Roberts F (1978) An efficient algorithm for discrete l1 linear approximation with linear constraints. SIAM J Numer Anal 15(3):603–611
Bosch M, Mukerji T, Gonzalez EF (2010) Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review. Geophysics 75(5):75A165–75A176
Brien OM, Sinclair AN, Kramer SM (1994) Recovery of a sparse spike time series by l/sub 1/norm deconvolution: IEEE Trans Signal Process 42:3353–3365
Brossier R, Operto S, Virieux J (2015) Velocity model building from seismic reflection data by full-waveform inversion. Geophys Prospect 63(2):354–367
Brown AR (2004) Interpretation of three-dimensional seismic data. AAPG Memoir 42. SEG Investigation in Geophysics, No. 9. AAPG, Tulsa
Clochard V, Delépine N, Labat K, Ricarte P (2009) Post-stack versus pre-stack stratigraphic inversion for CO2 monitoring purposes: a case study for the saline aquifer of the Sleipner field. SEG Annual Meeting, Society of Exploration Geophysicists
Cooke DA, Schneider WA (1983) Generalized linear inversion of reflection seismic data. Geophysics 48(6):665–676
Debeye H, Riel VP (1990) Lp-norm deconvolution: geophysical Prospecting 38:381–403
Ferguson RJ (1996) PS seismic inversion: modeling, processing and field examples. M.Sc. Thesis, University of Calgary, Canada
Ferguson RJ, Margrave GF (1996) A simple algorithm for band-limited impedance inversion. CREWES Res Rep 8(21):1–10
Goutsias J, Mendel JM (1986) Maximum-likelihood deconvolution: an optimization theory perspective. Geophysics 51:1206–1220
Hampson D, Russell B (1985) Maximum-likelihood seismic inversion. Geophysics 50(8):1380–1381
Helgesen J, Magnus I, Prosser S, Saigal G, Aamodt G, Dolberg D, Busman S (2000) Comparison of constrained sparse spike and stochastic inversion for porosity prediction at Kristin Field. Lead Edge 19(4):400–407
Kormylo JJ, Mendel JM (1983) Maximum-likelihood seismic deconvolution. IEEE Trans Geosci Remote Sens 1:72–82
Lancaster S, Whitcombe D (2000) Fast-track “colored” inversion. SEG Expanded Abstracts 19:1572–1575
Leite EP (2010) Seismic model based inversion using matlab. Matlab-Modelling, Programming and Simulations, p 389
Li Q (2001) LP sparse spike impedance inversion. Hampson-Russell Software Services Ltd, CSEG
Li LM, Speed TP (2004) Deconvolution of sparse positive spikes. J Comput Graph Statist 13(4):853–870
Loris I, Nolet G, Daubechies I, Dahlen FA (2007) Tomographic inversion using ℓ1-norm regularization of wavelet coefficients. Geophys J Int 170(1):359–370
Mallick S (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4):939–954
Maurya SP, Sarkar P (2016) Comparison of post-stack seismic inversion methods: a case study from Blackfoot Field, Canada. Int J Sci Eng Res 7(8):1091–1101
Maurya SP, Singh KH (2015) Reservoir characterization using model based inversion and probabilistic neural network. Discovery 49(228):122–127
Maurya SP, Singh KH (2015b) LP and ML sparse spike inversion for reservoir characterization-a case study from Blackfoot area, Alberta, Canada. In 77th EAGE Conference and Exhibition 2015 (1):1–5
Maurya SP, Singh NP (2017) Seismic colored inversion: a fast way to estimate rock properties from the seismic data. Carbonate Reservoir Workshop, Nov. 30th–Dec. 1th, 2017, IIT Bombay, India
Maurya SP, Singh NP (2018) Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution—A case study from the Blackfoot Field, Canada. J Appl Geophys, Elsevier 159 (2018):511–521
Maurya SP, Singh KH (2019) Predicting porosity by multivariate regression and probabilistic neural network using model-based and colored inversion as external attributes: a quantitative comparison. J Geol Soc India 93(2):131–252
Maurya SP, Singh KH, Singh NP (2019) Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada. Marine Geophys Res 40(1):51–71
Mendel JM (2012) Maximum-likelihood deconvolution: a journey into model-based signal processing. Springer Science & Business Media, Berlin
Oldenburg D, Scheuer T, Levy S (1983) Recovery of the acoustic impedance from reflection seismograms. Geophysics 48(10):1318–1337
Oliveira SAM, Lupinacci WM (2013) L1 norm inversion method for deconvolution in attenuating media. Geophys Prospect 61(4):771–777
Quijada MF (2009) Estimating elastic properties of sandstone reservoirs using well logs and seismic inversion. Doctoral dissertation, University of Calgary
Russell B (1988) Introduction to seismic inversion methods. The SEG Course Notes, Series 2
Russell B, Hampson D (1991) Comparison of post-stack seismic inversion methods. In: SEG technical program expanded abstracts, Society of Exploration Geophysicists, pp 876–878
Sacchi MD, Ulrych TJ (1995) High-resolution velocity gathers and offset space reconstruction. Geophysics 60(4):1169–1177
Sacchi MD, Ulrych TJ (1996) Estimation of the discrete fourier transform, a linear inversion approach. Geophysics 61(4):1128–1136
Stull RB (1973) Inversion rise model based on penetrative convection. J Atmos Sci 30(6):1092–1099
Swisi AA (2009) Post-and pre-stack attribute analysis and inversion of Blackfoot 3d seismic dataset. M.Sc. Thesis, University of Calgary
Velis DR (2006) Parametric sparse-spike deconvolution and the recovery of the acoustic impedance. In: SEG annual meeting. Society of Exploration Geophysicists, pp 2141–2144
Velis DR (2007) Stochastic sparse-spike deconvolution. Geophysics 73(1):R1–R9
Vestergaard PD, Mosegaard K (1991) Inversion of post-stack seismic data using simulated annealing. Geophys Prospect 39(5):613–624
Wang Y (2010) Seismic impedance inversion using l1-norm regularization and gradient descent methods. J Inverse Ill-Posed Prob 18(7):823–838
Wang X, Shiguo Wu, Ning Xu, Zhang G (2006) Estimation of gas hydrate saturation using constrained sparse spike inversion: case study from the Northern South China. Sea Terr Atmos Ocean Sci 17(4):799–813
Waters KH, Waters KH (1987) Reflection seismology: a tool for energy resource exploration. Wiley, New York
Yilmaz O (2001) Seismic data analysis, vol 1. Society of exploration geophysicists, Tulsa, OK
Zhang R, Castagna J (2011) Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics 76:R147–R158
Zhang Q, Yang R, Meng L, Zhang T, Li P (2016) The description of reservoiring model for gas hydrate based on the sparse spike inversion. In: 7th international conference on environmental and engineering geophysics & summit forum of Chinese Academy of Engineering on Engineering Science and Technology. https://doi.org/10.2991/iceeg-16.2016.27
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Maurya, S.P., Singh, N.P., Singh, K.H. (2020). Post-stack Seismic Inversion. In: Seismic Inversion Methods: A Practical Approach. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-030-45662-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-45662-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45661-0
Online ISBN: 978-3-030-45662-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)