Gas sand prediction using basis pursuit elastic impedance inversion
HAO Ya-Ju1(), GAO Jun2
1. School of Geophysics and Measurement-Control Technology,East China University of Technology,Nanchang 330013,China 2. Petroleum Exploration and Production Research Institute,SINOPEC,Beijing 100083,China
The impedance of sand reservoir will become lower if the pores are filled with natural gas,so the gas reservoir can be detected by using impedance inversion,which is a common used method.However,the impedance of sand reservoir can be influenced by many kinds of factors such as porosity and mineral composition.As a consequence,the impedance of gas sand may be higher than that of high porosity brine sandstone.In this case,mistake would be caused if post-stack impedance is only used to predict gas sand.Elastic impedance is more reliable than acoustic impedance,because gas sand reservoir can induce AVO anomaly.Simultaneously,in order to improve inversion resolution and accuracy,the authors introduce BP (Basis Pursuit) algorithm to complete elastic inversion.This algorithm is used to decompose seismic signal to even and odd wavelet dictionaries and then reflection coefficient can be obtained by the decomposition coefficients.The method proposed by the authors doesn't need initial low frequency model that traditional inversion method SSI (Sparse Spike Inversion) has to know beforehand. In this case,resolution and accuracy can be improved.The application to synthetic data and field data indicates that this method is more accurate than SSI.
Connolly P, Amoco B P. Elastic impedance[J]. The Leading Edge, 1999,18(4):438-452.
[2]
He F B, You J, Chen K Y. Gas sand distribution prediction by prestack elastic inversion based on rock physics modeling and analysis[J]. Applied Geophysics, 2011,8(3):197-205.
[3]
Hampson D, Schuelke J S, Quirein J A. Use of multi-attribute transforms to predict log properties from seismic data[J]. Exploration Geophysics, 2001,66(1):220-236.
Luan Y, Feng X, Liu C, et al. The research present and future of wave impedance inversion technique[J]. Journal of Jilin University:Earth Science Edition, 2008,38(s1):94-98.
Rui G S, Wang L, Tian W B. Improved algorithm based basis pursuit for compressive sensing reconstruction[J]. Electronic Measurement Technology, 2010,33(4):38-41.
Sun G C, Wang J L. Speaker recognition based on ARM[J]. Chinese Journal of Electron Devices, 2014,37(6):1151-1154.
[9]
Zhang R, Castagna J. Seismic sparse-layer reflectivity inversion using basis pursuit decomposition[J]. Geophysics, 2011,76(6):R147-R158.
[10]
Zhang R, Sen M K, Srinivasan S. A prestack basis pursuit seismic inversion[J]. Geophysics, 2013,78(1):R1-R11.
doi: 10.1190/geo2011-0502.1
[11]
Zoeppritz K. Ber reflexion und durchgang seismischer wellen durch unstetigkeitsflchen[J]. Nachrichten von der Kniglichen Gesellschaft der Wissenschaften zu Gttingen, 1919: 66-84.
[12]
Aki K, Richards P G. Quantitative seismology:Theory and methods[M]. W. H. Freeman, 1980.
[13]
Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. Society for Industrial and Applied Mathematics Review, 2001,43(1):129-159.
[14]
Hao Y, Huang H, Luo Y, et al. Nonstationary acoustic-impedance inversion algorithm via a novel equivalent Q-value estimation scheme and sparse regularizations[J]. Geophysics, 2018,83(6):R681-R698.
Shi Z Z, Xia Y Q, Zhou H L, et al. Seismic reflectivity inversion based on L1-L1-norm sparse representation[J]. Geophysical and Geochemical Exploration, 2019,43(4):851-858.