石油物探2024，Vol.63Issue(1)：12-29,18.DOI:10.12431/issn.1000-1441.2024.63.01.002
OBN地震数据成像处理基本逻辑与关键方法技术
Basic logic and key methods of OBN seismic data imaging processing
摘要
Abstract
Reservoirs in deep water and deep zones are becoming the targets of offshore oil and gas exploration,and there is an ur-gent need for the development of offshore seismic prospecting techniques owing to subsurface tectonic complexities(severe lateral variations),reservoir complexities(changing from structural reservoirs to structural,stratigraphic,and lithologic reservoirs),and seabed topographic and lithologic complexities.The primary issue in improving the efficiency of offshore exploration is to develop advanced techniques for seismic data acquisition and high-precision seismic imaging.Wide-azimuth wide-band high-density acquisi-tion and seismic imaging represented by full waveform inversion(FWI)/least squares reverse time migration(LS_RTM)are char-acteristic leading techniques in offshore and onshore seismic exploration.In offshore seismic exploration,OBN acquisition is gener-ally accepted to be the most feasible way to accomplish wide-azimuth wide-band high-density acquisition.Compared with streamer data acquisition,ocean bottom node(OBN)data acquisition features wide-azimuth illumination,high signal-to-noise ratio,no detec-tion-end ghosts,measured(at least first-order free surface related)downgoing wave field,and four-component observations,espe-cially wide-azimuth illumination and at least first-order free surface related downgoing wave field,which make it possible to achieve high-precision imaging of complicated middle and deep structures and near-seabed media.Our efforts focus on the requirements of high-precision imaging for seismic data acquisition,the necessity of OBN acquisition in offshore exploration,the characteristics of OBN seismic wave field,and the basic logic and corresponding key techniques for OBN data imaging.The particularities of marine data processing are supposed to be mainly caused by characteristic reflectors,which include seawater surface,seabed,and subsur-face strong reflecting horizons.We propose a technical solution to the prediction and suppression of multiples related to characteris-tic reflectors based on model-driven wave theory and compare some basic theories for multiples prediction.The linearized imaging operator-based prestack data domain and prestack imaging domain are supposed to be equivalent.Centering on the post processing of imaging gathers,expected imaging gathers are defined,and weak side lobes and quantitative reflection coefficients are taken as the targets of high-fidelity high-resolution imaging to achieve in-phase stacking of the wavelets from the same subsurface reflection(diffraction/scattering)point and different offsets in two domains and obtain imaging results of band-limited reflection coefficients with high fidelity and high resolution.It is suggested performing band-limited reflection coefficient imaging for broad-band imped-ance imaging.Based on the characteristics of OBN data,we present a basic workflow and key techniques of OBN data imaging.With respect to four-component OBN observations,the disagreements between wave phenomena in observed multi-component seismic wave field(mainly P_SV waves)and wave propagation and simulation theory lead to unsatisfactory multi-wave imaging.It is rec-ommended focusing on the physical origin of the disagreements between wave phenomena in observed multi-component seismic wave field and wave propagation and simulation theory instead of more advanced vector wave imaging algorithms.We hope that our ideas may promote further application of OBN data to offshore seismic exploration.关键词
海底节点(OBN)地震数据采集及成像处理/特征反射层相关多次波/模型驱动波动理论特征反射层相关多次波预测与压制/海底节点(OBN)地震数据成像处理流程及关键技术分类
天文与地球科学引用本文复制引用
王华忠,项健,石聿..OBN地震数据成像处理基本逻辑与关键方法技术[J].石油物探,2024,63(1):12-29,18.基金项目
国家自然科学基金(42174135,42074143,42304124)、中国博士后科学基金资助(2023M732633)、中海石油(中国)有限公司北京研究中心项目(CCL2021RCPS0436RSN)共同资助.This research is financially supported by the National Natural Science Foundation of China(Grant Nos.42174135,42074143,42304124),the China Postdoctoral Science Foundation(Grant No.2023M732633),the Project of Beijing Research Center of CNOOC(China)(Grant No.CCL2021RCPS0436RSN). （42174135,42074143,42304124）
