Extraction of strong beadlike reflections for a carbonate-karst reservoir using a tensor-based adaptive mathematical morphology. (18th August 2017)

• Record Type: Journal Article
• Authors: Wang, Yi; Lu, Wenkai; Wang, Benfeng; Chen, Jianjun; Ding, Jicai
• Is Part Of: Journal of geophysics and engineering. Volume 14:Number 5(2017:Oct.)
• Page Start: 1150
• Page End: 1159
• Publication Date: 2017-08-18
• Subjects: carbonate-karst reservoirs -- SBR -- mathematical morphology -- local structure tensor -- adaptive structure elements; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; Engineering -- Periodicals; 622.1505
• DOI: 10.1088/1742-2140/aa76d0
• Languages: English
• ISSNs: 1742-2132

Abstract

Abstract: Anomalously high-amplitude bright spots appear on seismic migration sections of carbonate-karst oil-bearing or gas-bearing reservoirs, also called the the string of beads response or strong beadlike reflections (SBRs). Although SBRs have distinct characteristics, it is not easy to detect and extract them from seismic migration sections because the seismic events and noise cannot be completely separated using conventional methods. An algorithm based on adaptive mathematical morphology is proposed in this paper to detect and extract the SBRs for the subsequent quantitative description of karst cave reservoirs. In our proposed method, the instantaneous amplitude section is first obtained with the Hilbert transform. Then, a tensor-based adaptive morphology is introduced to construct the adaptive structure elements which vary from linear to circular shapes according to the local structure tensor. To eliminate the effects of seismic events and noise, the orientations of our designed structural elements are rotated to be the same as the local dominant gradient directions of the instantaneous amplitude section. Finally, a top-hat transform, as a morphological operator, is used to extract the SBRs and remove the effects of seismic events and noise. We demonstrate the validity and effectiveness of our proposed method with field data examples.