{"title":"基于粒子群优化的多井小波同步反演","authors":"Huan Yuan, San-Yi Yuan, SuQin, Hong-Qiu Wang, Hua-Hui Zeng, Shi-Jun Yue","doi":"10.1007/s11770-024-1123-6","DOIUrl":null,"url":null,"abstract":"<p>Wavelet estimation is an important part of high-resolution seismic data processing. However, it is difficult to preserve the lateral continuity of geological structures and effectively recover weak geological bodies using conventional deterministic wavelet inversion methods, which are based on the joint inversion of wells with seismic data. In this study, starting from a single well, on the basis of the theory of single-well and multi-trace convolution, we propose a steady-state seismic wavelet extraction method for synchronized inversion using spatial multi-well and multi-well-side seismic data. The proposed method uses a spatially variable weighting function and wavelet invariant constraint conditions with particle swarm optimization to extract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve the spatial adaptability of the extracted wavelet and inversion stability. The simulated data demonstrate that the wavelet extracted using the proposed method is very stable and accurate. Even at a low signal-to-noise ratio, the proposed method can extract satisfactory seismic wavelets that reflect lateral changes in structures and weak effective geological bodies. The processing results for the field data show that the deconvolution results improve the vertical resolution and distinguish between weak oil and water thin layers and that the horizontal distribution characteristics are consistent with the log response characteristics.</p>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-well wavelet-synchronized inversion based on particle swarm optimization\",\"authors\":\"Huan Yuan, San-Yi Yuan, SuQin, Hong-Qiu Wang, Hua-Hui Zeng, Shi-Jun Yue\",\"doi\":\"10.1007/s11770-024-1123-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Wavelet estimation is an important part of high-resolution seismic data processing. However, it is difficult to preserve the lateral continuity of geological structures and effectively recover weak geological bodies using conventional deterministic wavelet inversion methods, which are based on the joint inversion of wells with seismic data. In this study, starting from a single well, on the basis of the theory of single-well and multi-trace convolution, we propose a steady-state seismic wavelet extraction method for synchronized inversion using spatial multi-well and multi-well-side seismic data. The proposed method uses a spatially variable weighting function and wavelet invariant constraint conditions with particle swarm optimization to extract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve the spatial adaptability of the extracted wavelet and inversion stability. The simulated data demonstrate that the wavelet extracted using the proposed method is very stable and accurate. Even at a low signal-to-noise ratio, the proposed method can extract satisfactory seismic wavelets that reflect lateral changes in structures and weak effective geological bodies. The processing results for the field data show that the deconvolution results improve the vertical resolution and distinguish between weak oil and water thin layers and that the horizontal distribution characteristics are consistent with the log response characteristics.</p>\",\"PeriodicalId\":55500,\"journal\":{\"name\":\"Applied Geophysics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geophysics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s11770-024-1123-6\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11770-024-1123-6","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Multi-well wavelet-synchronized inversion based on particle swarm optimization
Wavelet estimation is an important part of high-resolution seismic data processing. However, it is difficult to preserve the lateral continuity of geological structures and effectively recover weak geological bodies using conventional deterministic wavelet inversion methods, which are based on the joint inversion of wells with seismic data. In this study, starting from a single well, on the basis of the theory of single-well and multi-trace convolution, we propose a steady-state seismic wavelet extraction method for synchronized inversion using spatial multi-well and multi-well-side seismic data. The proposed method uses a spatially variable weighting function and wavelet invariant constraint conditions with particle swarm optimization to extract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve the spatial adaptability of the extracted wavelet and inversion stability. The simulated data demonstrate that the wavelet extracted using the proposed method is very stable and accurate. Even at a low signal-to-noise ratio, the proposed method can extract satisfactory seismic wavelets that reflect lateral changes in structures and weak effective geological bodies. The processing results for the field data show that the deconvolution results improve the vertical resolution and distinguish between weak oil and water thin layers and that the horizontal distribution characteristics are consistent with the log response characteristics.
期刊介绍:
The journal is designed to provide an academic realm for a broad blend of academic and industry papers to promote rapid communication and exchange of ideas between Chinese and world-wide geophysicists.
The publication covers the applications of geoscience, geophysics, and related disciplines in the fields of energy, resources, environment, disaster, engineering, information, military, and surveying.