Application of seismic frequency-divided iterative inversion in the prediction of thinly laminated channel sand bodies
-
摘要: 龙凤山地区河道砂储层符合典型岩性油气藏特征,其砂体厚度薄、河道窄、岩性纵横向非均质性强,对5 m以下储层预测难度大。分频迭代反演充分利用全频段地震资料,对不同频段、尺度的地震信息逐级传递,优化反演结果。文中具体利用匹配追踪算法实现地震信号频段划分得到不同尺度地震数据体,在测井约束下,以低频大尺度的反演结果作为下一级频段反演的初始模型,调整反演结果;在反演过程中,通过相关算法自适应选取子波,增强反演准确性,基于贝叶斯理论自适应选取正则化参数,调节分辨率和稳定性关系达到最佳平衡,避免反演出现混沌现象。2019年该区新钻四口井钻遇营城组1-2-6和1-2-8小层的气层,同反演预测结果吻合。证明本文方法相较于常规分频反演而言,具有反演精度高、忠实于地震信息、频段应用充分的优点,可以有效提高薄层河道砂的识别能力,指导相关岩性油气藏的勘探开发。Abstract: 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.
-
-
[1] 于建国, 韩文功, 刘力辉. 分频反演方法及应用[J]. 石油地球物理勘探, 2006, 41(2):193-197.
[2] Yu J G, Han W G, Liu L H. Crossover inversion method and application[J]. Oil Geophysical Prospecting, 2006, 41(2):193-197.
[3] 吴媚, 李维新, 符力耘. 基于测井曲线分频分析的地震反演[J]. 石油地球物理勘探, 2007, 42(S1):65-71.
[4] Wu M, Li W X, Fu L Y. Seismic inversion based on logging curve crossover analysis[J]. Oil Geophysical Prospecting, 2007, 42(S1):65-71.
[5] 龚洪林, 王振卿, 蔡刚, 等. 分频解释技术在碳酸盐岩储层预测中应用[J]. 西南石油大学学报:自然科学版, 2007, 29(S1):5-8.
[6] 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.
[7] 王振卿, 王宏斌, 张虎权, 等. 分频波阻抗反演技术在塔中西部台内滩储层预测中的应用[J]. 天然气地球科学, 2014, 25(11):171-178.
[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.
[9] 朱超, 刘占国, 杨少勇, 等. 利用相控分频反演预测英西湖相碳酸盐岩储层[J]. 石油地球物理勘探, 2018, 53(4):187-196.
[10] 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.
[11] 倪祥龙, 黄成刚, 杜斌山, 等. 盆缘凹陷区甜点储层主控因素与源下成藏模式——以柴达木盆地扎哈泉地区渐新统为例[J]. 中国矿业大学学报, 2019, 48(1):156-167.
[12] 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.
[13] 代玲, 万钧, 罗泽. 分频反演精细储层预测[J]. 中外能源, 2021, 26(12):48-53.
[14] Dai L, Wan J, Luo Z. Detailed reservoir prediction of crossover inversion[J]. Sino-Foreign Energy, 2021, 26(12):48-53.
[15] 肖曦. 分频迭代宽频反演方法在储层预测中的应用[J]. 地球物理学进展, 2021, 36(1):294-299.
[16] Xiao X. Application of crossover iterative broadband inversion method in reservoir prediction[J]. Progress in Geophysics, 2021, 36(1):294-299.
[17] 黄捍东, 张如伟, 魏世平. 地震非线性随机反演方法在陆相薄砂岩储层预测中的应用[J]. 石油学报, 2009, 30(3):386-390.
[18] 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.
[19] 印兴耀, 裴松, 李坤, 等. 多尺度快速匹配追踪多域联合地震反演方法[J]. 地球物理学报, 2020, 63(9):3431-3441.
[20] 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.
[21] 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.
[22] Daubechies I. Orthonormal bases of compactly supported wavelets[J]. Communications on Pure and Applied Mathematics, 1988, 41(7):909-996.
[23] 张繁昌, 李传辉. 非平稳地震信号匹配追踪时频分析[J]. 物探与化探, 2011, 35(4):120-126.
[24] 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.
[25] 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.
[26] Chen S Q. Application of matching tracking algorithm in seismic data processing[D]. Beijing: China University of Petroleum(Beijing), 2018.
[27] Liu J, Marfurt K J. Matching pursuit decomposition using Morlet wavelets[C]// SEG Technical Program Expanded Abstracts, 1949, 24:786.
[28] Wang Y H. Seismic-time frequency spectral decomposition by matching pursuit[J]. Geophysics, 2007, 72(1):13-20
[29] 黄捍东, 郭飞, 汪佳蓓, 等. 高精度地震时频谱分解方法及应用[J]. 石油地球物理勘探, 2012, 47(5):773-780.
[30] 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.
-
计量
- 文章访问数: 788
- PDF下载数: 133
- 施引文献: 0