Abstract
Fault zone refers to the fracture zone in the stratum, which is of great significance to the migration and accumulation of hydrocarbons. Each fault zone has a unique, complex structure. Hence, the hydrocarbons migrate and accumulate differently from fault zone to fault zone. The accurate division of fault zone structure is critical to understanding fault-controlled hydrocarbon reservoirs. Currently, the fault zone structure is often roughly identified by logging, based on a summary of fuzzy response modes. The innovation of this paper lies in the following: according to the macro- and micro-characteristics of structural units with different lithology, different logging responses, and core data in Jiyang Depression, a fault zone structure identification method based on wavelet attributes of logging data is proposed. Through multi-scale processing, the proposed method reconstructs and highlights the high-frequency information of curve fluctuations on the appropriate scale, enabling the accurate identification of fracture development intervals. Taking Che-57 well which passes through fault zone in Jiyang Depression of Shengli Oilfield as an example, the structure of fault zone is identified by wavelet attribute. The results show that the three porosity wavelet attributes could accurately characterize the structure of the fault zone; the sliding fracture zone has a low frequency and a low amplitude; the induced fracture zone has a high frequency and a high amplitude; the density and acoustic wavelet attribute are more sensitive than the electrical curve. The research results shed new light on the identification of fault zone structure in carbonate stratum.
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28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08472-7
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This research was funded by the Postdoctoral project of Shengli Oilfield YKB2113.
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Responsible Editor: Ahmed Farouk
This article is part of the Topical Collection on Big Data and Intelligent Computing Techniques in Geosciences
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Xu, X., Yan, D., Qi, J. et al. Structural identification of fault zones based on wavelet attributes of logging data. Arab J Geosci 14, 1219 (2021). https://doi.org/10.1007/s12517-021-07513-5
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DOI: https://doi.org/10.1007/s12517-021-07513-5