一种基于时频域波形分类的储层预测方法

doi:10.12108/yxyqc.20180411

岩性油气藏 ›› 2018, Vol. 30 ›› Issue (4): 98–104.doi: 10.12108/yxyqc.20180411

一种基于时频域波形分类的储层预测方法

石战战^1,2, 王元君², 唐湘蓉², 庞溯¹, 池跃龙^1,2

1. 成都理工大学工程技术学院, 四川乐山 614000;
2. 成都理工大学地球物理学院, 成都 610059

收稿日期:2018-01-20 修回日期:2018-03-15 出版日期:2018-07-21 发布日期:2018-07-21
第一作者:石战战(1986-),男,成都理工大学地球物理学院在读博士研究生,讲师,主要从事储层预测方面的科研和教学工作。地址:(614000)四川省乐山市市中区肖坝路222号成都理工大学工程技术学院资源勘查与土木工程系。Email:shizhanzh@163.com。
基金资助:
国家自然科学基金项目“孔隙介质低频地震衰减与频散异常的识别机理及应用”（编号：41374134）、“复杂地震信号分数域频谱成像理论及应用研究”（编号：41274127）及四川省教育厅项目“基于时频域波形分类的礁滩储层预测方法研究”（编号：16ZB0410）联合资助

Reservoir prediction based on seismic waveform classification in time-frequency domain

SHI Zhanzhan^1,2, WANG Yuanjun², TANG Xiangrong², PANG Su¹, CHI Yuelong^1,2

1. The Engineering and Technical College, Chengdu University of Technology, Leshan 614000, Sichuan, China;
2. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China

Received:2018-01-20 Revised:2018-03-15 Online:2018-07-21 Published:2018-07-21

摘要/Abstract

摘要： 传统时频分析方法在储层预测中面临以下2个问题：受Heisenberg测不准原理或交叉项的影响，常难以满足分辨率要求；增加了信号的冗余度，频域采样率越高，信号冗余度越高，解释工作量就越大。为了解决这2个问题，提出基于时频域波形分类的储层预测方法，该方法通过同步提取变换对地震信号进行时频谱分解，相当于将复杂信号分解为一系列（不同频率和不同时移量的）简单波形的叠加，并对分解结果利用生成拓扑映射进行分类，进而通过测井、钻井资料标定波形分类结果。该方法能够有效检测地震信号波形变化、精细刻画储层形态。

关键词: 曲流河, 隔夹层, 开发中后期, 剩余油, 挖潜, 秦皇岛油田

Abstract: There are two problems in the reservoir prediction:(1)The traditional time-frequency analysis method is influenced by Heisenberg uncertainty principle or cross-terms,so it is often difficult to meet the requirements of high resolution.(2)The traditional time-frequency analysis method increases the redundancy of signal and increases the interpretation workload. Aiming at these two problems,a reservoir prediction method based on timefrequency domain seismic waveform classification was proposed. This method performs time-frequency decomposition of seismic signal by synchroextracting transform,and it corresponds to decomposing the complex signals into a series of simple waveforms(different frequencies and time shifts)superimposed. The decomposition results were classified by generative topographic mapping,and the waveform classification results were further calibrated by logging and drilling data. The practical applications indicate that the seismic signal waveform changes can be effectively detected by the reservoir prediction method combined with the synchroextracting transform and the generative topographic mapping.

Key words: meandering river, interlayer, middle-late stage of development, remaining oil, tapping the potential, Qinhuangdao oilfield

中图分类号:

TE132.1+4

石战战, 王元君, 唐湘蓉, 庞溯, 池跃龙. 一种基于时频域波形分类的储层预测方法[J]. 岩性油气藏, 2018, 30(4): 98–104.

SHI Zhanzhan, WANG Yuanjun, TANG Xiangrong, PANG Su, CHI Yuelong. Reservoir prediction based on seismic waveform classification in time-frequency domain[J]. Lithologic Reservoirs, 2018, 30(4): 98–104.

参考文献

[1] 熊翥. 地层、岩性油气藏地震勘探方法与技术. 石油地球物理勘探, 2012, 47(1):1-18. XIONG Z. Seismic exploration for strati-lithologic reservoirs. Oil Geophysical Prospecting, 2012, 47(1):1-18.
[2] ZHAO T,ROY A,JAYARAM V, et al. A comparison of classification techniques for seismic facies recognition. Interpretation, 2015, 3(4):SAE29-SAE58.
[3] BOASHASH B. Time-frequency signal analysis and processing:a comprehensive reference. London:Academic Press, 2015.
[4] 张猛刚, 洪忠, 窦玉坛, 等. 时频分析在苏里格地区含气性检测中的应用. 岩性油气藏, 2013, 25(5):76-80. ZHANG M G, HONG Z, DOU Y T, et al. Application of timefrequency analysis technology to the gas detection in Sulige area. Lithologic Reservoirs, 2013, 25(5):76-80.
[5] 陈学华, 贺振华, 黄德济, 等.时频域油气储层低频阴影检测. 地球物理学报, 2009, 52(1):215-221. CHEN X H, HE Z H, HUANG D J, et al. Low frequency shadow detection of gas reservoirs in time-frequency domain. Chinese Journal of Geophysics, 2009, 52(1):215-221.
[6] 王德营, 李振春, 王姣. S域时频滤波分析与改进. 石油物探, 2015, 54(4):396-403. WANG D Y, LI Z C, WANG J. The analysis and improvement on time-frequency filtering in S-transform domain. Geophysical Prospecting for Petroleum, 2015, 54(4):396-403.
[7] 高刚, 李玉海, 桂志先, 等.基于广义S变换频散AVO属性提取方法研究.岩性油气藏, 2015, 27(4):84-88. GAO G, LI Y H, GUI Z X, et al. Abstraction of frequency-dependent AVO attributes based on generalized S transform. Lithologic Reservoirs, 2015, 27(4):84-88.
[8] 陈胜, 欧阳永林, 曾庆才, 等.匹配追踪子波分解重构技术在气层检测中的应用. 岩性油气藏, 2014, 26(6):111-114. CHEN S, OUYANG Y L, ZENG Q C, et al. Application of matching pursuit wavelet decomposition and reconstruction technique to reservoir prediction and gas detection. Lithologic Reservoirs, 2014, 26(6):111-114.
[9] LI Y D, ZHENG X D. Wigner-Ville distribution and its application in seismic attenuation estimation. Applied Geophysics, 2007, 4(4):245-254.
[10] MANDIC D P, REHMAN N U, WU Z, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals:the power of adaptive data analysis. IEEE Signal Processing Magazine, 2013, 30(6):74-86.
[11] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2011:4144-4147.
[12] BATTISTA B, KNAPP C, MCGEE T, et al. Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data. Geophysics, 2007, 72(2):H29-H37.
[13] CHEN Y, ZHOU C, YUAN J, et al. Applications of empirical mode decomposition in random noise attenuation of seismic data. Journal of Seismic Exploration, 2014, 23(6):481-495.
[14] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.
[15] GILLES J. Empirical wavelet transform. IEEE Transactions on Signal Processing, 2013, 61(16):3999-4010.
[16] AUGER F, FLANDRIN P. Improving the readability of timefrequency and time-scale representations by the reassignment method. IEEE Transactions on Signal Processing, 1995, 43(5):1068-1089.
[17] DAUBECHIES I, LU J, WU H T. Synchrosqueezed wavelet transforms:an empirical mode decomposition-like tool. Applied & Computational Harmonic Analysis, 2011, 30(2):243-261.
[18] HERRERA R H, HAN J, MIRKO V D B. Applications of the synchrosqueezing transform in seismic time-frequency analysis. Geophysics, 2014, 79(3):V55-V64.
[19] YU G, YU M, XU C. Synchroextracting transform. IEEE Transactions on Industrial Electronics, 2017, 64(10):8042-8054.
[20] BALCH A H. Color sonagrams:a new dimension in seismic data interpretation. Geophysics, 1971, 36(6):1074-1098.
[21] COLEOU T, POUPON M, AZBEL K. Unsupervised seismic facies classification:a review and comparison of techniques and implementation. The Leading Edge, 2003, 22(10):942-953.
[22] GAO D. Application of three-dimensional seismic texture analysis with special reference to deep-marine facies discrimination and interpretation:Offshore Angola,west Africa. AAPG Bulletin, 2007, 91(12):1665-1683.
[23] ROY A, DOWDELL B L, MARFURT K J. Characterizing a Mississippian tripolitic chert reservoir using 3 D unsupervised and supervised multiattribute seismic facies analysis:an example from Osage County, Oklahoma. Interpretation, 2013, 1(2):SB109-SB124.
[24] KOHONEN T. Self-organizing maps. Berlin:Springer-Verlag, 1995.
[25] VATANEN T, OSMALA M, RAIKO T, et al. Self-organization and missing values in SOM and GTM. Neurocomputing, 2015, 147(6/7):60-70.
[26] GISBRECHT A, HAMMER B. Relevance learning in generative topographic mapping. Neurocomputing, 2011, 74(9):1351-1358.
[27] BISHOP C, SVENSÉN M, WILLIAMS C. GTM:the generative topographic mapping. Neural Computation, 1998, 10(1):215-234.
[28] WALLET B C, MATOS M C D, KWIATKOWSKI J T, et al. Latent space modeling of seismic data:an overview. The Leading Edge, 2009, 28(12):1454-1459.
[29] ROY A, ROMERO-PELÁEZ A S, KWIATKOWSKI T J, et al. Generative topographic mapping for seismic facies estimation of a carbonate wash, Veracruz Basin, southern Mexico. Interpretation, 2014, 2(2):SA31-SA47.
[30] LIU Y. Seismic"low frequency shadows"for gas sand reflection. Denver:SEG, 2004.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

一种基于时频域波形分类的储层预测方法

Reservoir prediction based on seismic waveform classification in time-frequency domain

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	徐中波, 汪利兵, 申春生, 陈铭阳, 甘立琴. 渤海蓬莱19-3油田新近系明下段曲流河储层构型表征[J]. 岩性油气藏, 2023, 35(5): 100-107.
[2]	姚秀田, 王超, 闫森, 王明鹏, 李婉. 渤海湾盆地沾化凹陷新生界断层精细表征及地质意义[J]. 岩性油气藏, 2023, 35(4): 50-60.
[3]	张昌民, 张祥辉, ADRIAN J. Hartley, 冯文杰, 尹太举, 尹艳树, 朱锐. 分支河流体系分类初探[J]. 岩性油气藏, 2023, 35(4): 1-15.
[4]	马东烨, 陈宇航, 赵靖舟, 吴伟涛, 宋平, 陈梦娜. 鄂尔多斯盆地东部二叠系下石盒子组8段河流相砂体构型要素[J]. 岩性油气藏, 2023, 35(1): 63-73.
[5]	任梦怡, 胡光义, 范廷恩, 范洪军. 秦皇岛32-6油田北区新近系明化镇组下段复合砂体构型及控制因素[J]. 岩性油气藏, 2022, 34(6): 141-151.
[6]	孟智强, 葛丽珍, 祝晓林, 王永平, 朱志强. 气顶边水油藏气/水驱产油量贡献评价方法[J]. 岩性油气藏, 2022, 34(5): 162-170.
[7]	李晓辉, 杜晓峰, 官大勇, 王志萍, 王启明. 辽东湾坳陷东北部新近系馆陶组辫曲过渡型河流沉积特征[J]. 岩性油气藏, 2022, 34(3): 93-103.
[8]	易志凤, 张尚锋, 王雅宁, 徐恩泽, 赵韶华, 王玉瑶. 差异曲率下的曲流河点坝砂体定量表征——以黄河源区白河现代沉积为例[J]. 岩性油气藏, 2022, 34(1): 34-42.
[9]	张皓宇, 李茂, 康永梅, 吴泽民, 王广. 鄂尔多斯盆地镇北油田长3油层组储层构型及剩余油精细表征[J]. 岩性油气藏, 2021, 33(6): 177-188.
[10]	王静怡, 周志军, 魏华彬, 崔春雪. 基于页岩孔隙网络模型的油水两相流动模拟[J]. 岩性油气藏, 2021, 33(5): 148-154.
[11]	王立辉, 夏惠芬, 韩培慧, 曹瑞波, 孙先达, 张思琪. 剩余油分布的微观特征及其可动用程度的定量表征[J]. 岩性油气藏, 2021, 33(2): 147-154.
[12]	邓猛, 邵英博, 赵军寿, 廖辉, 邓琪. 渤海A油田明化镇组下段河-坝砂体储层构型及剩余油分布[J]. 岩性油气藏, 2020, 32(6): 154-163.
[13]	吕文睿, 张峰, 纪友亮, 周勇, 罗妮娜, 张艺楼, 梁星如, 程煜宗. 饶阳凹陷大王庄地区沙一上亚段河口坝结构对油藏开发的影响[J]. 岩性油气藏, 2020, 32(4): 143-154.
[14]	龙明, 刘英宪, 陈晓祺, 王美楠, 于登飞. 基于曲流河储层构型的注采结构优化调整[J]. 岩性油气藏, 2019, 31(6): 145-154.
[15]	涂乙, 王亚会, 闫正和, 高永明, 魏启任. 基于构型单元“势控论”研究与剩余油开发效果分析[J]. 岩性油气藏, 2019, 31(4): 133-140.