An improved reverse time migration for subsurface imaging over complex geological structures: A numerical study

被引:2
作者
Routa, Alok Kumar [1 ]
Monahty, Priya Ranjan [1 ]
机构
[1] IIT ISM Dhanbad, Dept Appl Geophys, Jharkhand, India
关键词
Imaging condition; Reverse time migration (RTM); Seismic imaging; Velocity -depth model; Wave propagation; EXTRAPOLATION; GATHERS;
D O I
10.1016/j.engeos.2023.100239
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In seismic exploration, it is a critical task to image and interpret different seismic signatures over complex geology due to strong lateral velocity contrast, steep reflectors, overburden strata and dipping flanks. To understand the behavior of these seismic signatures, nowadays Reverse Time Migration (RTM) technique is used extensively by the oil & gas industries. During the extrapolation phase of RTM, the source wavefield needs to be saved, which needs high storage memory and large computing time. These two are the main obstacles of RTM for production use. In order to overcome these disadvantages, in this study, a second-generation improved RTM technique is proposed. In this improved form, a shift operator is introduced at the time of imaging condition of RTM algorithm which is performed automatically both in space and time domain. This effort is made to produce a better-quality image by minimizing the computational time as well as numerical artefacts. The proposed method is applied over various benchmark models and validated by implementing over one field data set from the Jaisalmer Basin, India. From the analysis, it is observed that the method consumes a minimum of 45% less storage space and reduce the execution time by 20%, as compared to conventional RTM. The proposed RTM is found to work efficiently in comparison to the conventional RTM both in terms of imaging quality and minimization of numerical artefacts for all the benchmark models as well as field data. (c) 2023 Sinopec Petroleum Exploration and Production Research Institute. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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页数:8
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