摘要
角度道集是叠前深度偏移成像结果的高维表达形式,对于储层描述具有重要意义。高效性和保真性是生成逆时偏移角度道集值得关注的两个方面。介绍了两种常见的逆时偏移角度道集生成方法:波矢量方向估计方法及局部波场分解方法。前者计算效率高,但是对噪声比较敏感,无法处理波场交叉情况;后者可处理复杂波场交叉情况,角度道集质量较高,但是计算效率较低。结合两种方法的特点,提出了一种混合生成逆时偏移角度域共成像点道集(RTM-ADCIGs)的方法。首先利用局部波场分解获得局部波前方向,然后利用坡印廷矢量方法确定波的传播方向,实现效率和质量的折中。在此基础上,利用结构张量特征值快速判断波场交叉情况,只在波场能量交叉情况下使用混合方法,估计一个或多个局部波场的方向,而在其它区域采用快速波矢量估计方法提取波场方向,进一步提高角度道集的计算效率。最后利用Sigsbee模型对比分析了不同方法的效果和效率,并在水平层状模型上测试了本文方法的保幅性。
Angle domain common image gathers(ADCIGs) are the high-dimensional forms for the result of prestack depth migration, and very important for further reservoir characterization. Efficiency and fidelity are the focus aspects on the generation of AD-CIGs. For generation of RTM-ADCIGs, two methods were reviewed, namely direction vector-based method and local wavefield decomposition method. Direction vector based method is efficient, but it is too sensitive to noise and difficult to handle the overlapping events. Moreover, its output gathers are mostly with low qualities if no additional regularizations are applied. Local wavefield decomposition method generates high-quality gathers,but with low efficiency.In view of the situation,a hybrid approach is proposed for obtaining the common image gathers based on RTM. Firstly, the wavefront orientations are obtained by local wavefield decompositions. Then, the Poynting vectors are used to determine propagation directions and to achieve a good compromise between the cefficiency and quality. The eigenvalues of structure tensor are used to identify the wavefield overlapping events. In the region of overlapping wavefronts,the hybrid method is employed to extract one or more local plane wavefield. In other regions,he propagation directions are calculated by means of efficient direction vector-based method to further improve the efficiency. Tests on Sigsbee model and layered model data showed that this approach can generate ADCIGs efficiently with high accuracy and high fidelity.
作者
吴成梁
周阳
胡江涛
王华忠
孙荣
WU Chengliang;ZHOU Yang;HU Jiangtao;WANG Huazhong;SUN Rong(Wave Phenomena and Inversion Imaging Research Group(WPI),School of Ocean and Earth Science,Tongji University,Shanghai 200092 China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation.Chengdu University of Technology,Chengdu 6100S9,China;CNPC Research Institute of Petroleum Exploration & Developmert CNPC,Beijing 100083,China)
出处
《石油物探》
EI
CSCD
北大核心
2018年第3期404-418,共15页
Geophysical Prospecting For Petroleum
基金
国家自然科学基金(41774126)和国家科技重大专项(2016ZX05024-001,2016ZX05006-002)联合资助.