Application of seismic frequency-divided iterative inversion in the prediction of thinly laminated channel sand bodies
REN Xian-Jun1(), LI Zhong2(), MA Ying-Long3, DONG Ping4, TIAN Xing-Da5
1. Exploration and Development Research Institute,Sinopec Northeast Oil and Gas Company,Changchun 130062,China 2. Data Processing Center(Zhanjiang),Research Institute,Geophysical Branch,China Oilfield Services Limited,CNOOC,Zhanjiang 524057,China 3. Research Institute of Exploration Development,PetroChina Tarim Oilfield Company,Kuerle 841000,China 4. Engineering Technology Research Institute,No. 3 Oil Production Plant of Huabei Oilfileld Company,Cangzhou 062450,China 5. Changjiang Geophysical Exploration & Testing Co.,Ltd.,Wuhan 430010,China
The channel sand reservoirs in the Longfengshan area have the characteristics of typical lithologic reservoirs.This area has thin sand bodies,narrow channels,and strong vertical and horizontal lithologic heterogeneity.It is difficult to predict the reservoirs at a depth of 5 m or greater.The frequency-divided iterative inversion can fully utilize the full-frequency band seismic data and transmit the seismic information of different frequency bands and scales step by step,thus optimizing the inversion results.In this study,the seismic signal frequency bands were divided using the matching pursuit algorithm to obtain seismic data volumes of different scales.Under the constraints of log data,the low-frequency,large-scale inversion results were used as the initial model for the next-order frequency band inversion,and the inversion results.During the inversion,wavelets were adaptively selected using the correlation algorithm to enhance the inversion accuracy.Regularization parameters were adaptively selected based on the Bayesian theory to adjust the relationship between resolution and stability to achieve the optimal balance and avoid chaos in inversion.In 2019,gas reservoirs in subzones 1-2-6 and 1-2-8 of the Yingcheng Formation were encountered in the drilling of four wells in the Longfengshan area.This result is consistent with the inversion prediction results.Therefore,compared with conventional frequency division inversion,the method proposed in this study has the advantages of high inversion accuracy,coincidence with seismic information,and full application of frequency bands.This method can effectively improve the identification performance of thinly laminated channel sand bodies and guide the exploration and development of related lithologic reservoirs.
Gong H L, Wang Z Q, Cai G, et al. Application of crossover interpretation technique in carbonate reservoir prediction[J]. Journal of Southwest Petroleum University:Natural Science Edition, 2007, 29(S1):5-8.
Wang Z Q, Wang H B, Zhang H Q, et al. Application of crossover wave impedance inversion technology in reservoir prediction of Tainei Bund in the western of Tazhong[J]. Natural Gas Geoscience, 2014, 25(11):171-178.
Zhu C, Liu Z G, Yang S Y, et al. Prediction of Yingxi Lake phase carbonate reservoir by phased crossover inversion[J]. Oil Geophysical Prospecting, 2018, 53(4):187-196.
Ni X L, Huang C G, Du B S, et al. Main controlling factors of sweet spot reservoir in basin margin depression and reservoir forming model under source:Taking Oligocene in Zhahaquan area,Qaidam basin as an example[J]. Journal of China University of Mining and Technology, 2019, 48(1):156-167.
Huang H D, Zhang R W, Wei S P. Application of seismic nonlinear stochastic inversion method in prediction of continental thin sandstone reservoirs[J]. Acta Petroleum Sinica, 2009, 30(3):386-390.
Yin X Y, Pei S, Li K, et al. Multi-domain joint seismic inversion method for multi-scale rapid matching tracking[J]. Chinese Journal of Geophysics, 2020, 63(9):3431-3441.
[11]
Mallat S G. A theory for multiresolution signal decomposition:The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693.
doi: 10.1109/34.192463
[12]
Daubechies I. Orthonormal bases of compactly supported wavelets[J]. Communications on Pure and Applied Mathematics, 1988, 41(7):909-996.
doi: 10.1002/(ISSN)1097-0312
Zhang F C. LI C H. Time-frequency analysis of non-stationary seismic signals by matching pursuit[J]. Geophysical and Geochemical Exploration, 2011, 35(4):120-126.
[14]
陈姝荞. 匹配追踪算法在地震资料处理中的应用[D]. 北京: 中国石油大学(北京), 2018.
[14]
Chen S Q. Application of matching tracking algorithm in seismic data processing[D]. Beijing: China University of Petroleum(Beijing), 2018.
[15]
Liu J, Marfurt K J. Matching pursuit decomposition using Morlet wavelets[C]// SEG Technical Program Expanded Abstracts, 1949, 24:786.
[16]
Wang Y H. Seismic-time frequency spectral decomposition by matching pursuit[J]. Geophysics, 2007, 72(1):13-20
Huang H D, Guo F, Wang J B, et al. Spectral decomposition method and application of high-precision seismic time[J]. Oil Geophysical Prospecting, 2012, 47(5):773-780.
