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A new energy density calculation method for reservoir delineation using seismic low‐frequency signal
Author(s) -
Luo Xin,
Chen Xuehua,
Qi Yingkai,
Yang Jidong,
Zhang Jie,
Jiang Wei
Publication year - 2020
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.13005
Subject(s) - geology , attenuation , low frequency , energy (signal processing) , environmental geology , range (aeronautics) , seismic wave , economic geology , regional geology , signal (programming language) , seismology , physics , statistics , optics , computer science , materials science , mathematics , metamorphic petrology , telmatology , astronomy , composite material , tectonics , programming language
In comparison to high‐frequency signals, low‐frequency seismic signals suffer less from scattering and intrinsic attenuation during wave propagation, penetrate deeper strata and thus can provide more energy information related to the hydrocarbon reservoirs. Based on the asymptotic representation for the frequency‐dependent reflections in the fluid‐saturated pore‐elastic media, we first derive a novel equation of the reservoir energy density and present an efficient workflow to calculate the reservoir energy density using low‐frequency seismic data. Then, within a low‐frequency range (from 1 to 30 Hz), we construct an objective function to determine the optimal frequency, using the energy densities calculated from the post‐stack seismic traces close to the wells. Next, we can calculate the reservoir energy density using the instantaneous spectra of optimal frequency at the low‐frequency end of the seismic spectrum. Tests on examples for synthetic and field data demonstrate that the proposed reservoir energy density can produce high‐quality images for the fluid‐saturated reservoirs, and it produces less background artefacts caused by elastic layers. This method provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.