Seismic Attribute Analyses and Attenuation Applications for Detecting Gas Hydrate Presence

Abstract

Identifying gas hydrates in the oceanic subsurface using seismic reflection data supported by the presence of a bottom simulating reflector (BSR) is not an easy task, given the wide range of geophysical methods that have been applied to do so. Though the presence of the BSR is attributed to the attenuation response, as seismic waves transition from hydrate-filled sediment within the gas hydrate stability zone (GHSZ) to free gas-bearing sediment below, few studies have applied a direct attenuation measurement. To improve the detection of gas hydrates and associated features, including the BSR and free gas accumulation beneath the gas hydrates, we apply a recently developed method known as Sparse-Spike Decomposition (SSD) that directly measures attenuation from estimating the quality factor (Q) parameter. In addition to performing attribute analyses using frequency attributes and a spectral decomposition method to improve BSR imaging, using a comprehensive analysis of the three methods, we make several key observations. These include the following: (1) low-frequency shadow zones seem to correlate with large values of attenuation; (2) there is a strong relationship between the amplitude strength of the BSR and the increase of the attenuation response; (3) the resulting interpretation of migration pathways of the free gas using the direct attenuation measurement method; and (4) for the data analyzed, the gas hydrates themselves do not give rise to either impedance or attenuation anomalies that fully differentiate them from nearby non-hydrate zones. From this last observation, we find that, although the SSD method may not directly detect in situ gas hydrates, the same gas hydrates often form an effective seal trapping and deeper free gas accumulation, which can exhibit a large attenuation response, allowing us to infer the likely presence of the overlying hydrates themselves.

Sedimentary bottom simulating reflection muting—A new model of hydrate and fluid redistribution from the Pegasus Basin, New Zealand

“…The use of seismic attributes that measure anelastic seismic energy attenuation showed a possible absence of accumulated free gas within synclines at the depth of the BSR. Clairmont et al (2021) suggest that previous interpretations by Katz (1981) of synclinal fluid distribution principles explain a lack of free gas across large synclines. Our new model supports interpretations and modelling by Clairmont et al (2021) and Kroeger et al (2022).…”

“…Clairmont et al (2021) suggest that previous interpretations by Katz (1981) of synclinal fluid distribution principles explain a lack of free gas across large synclines. Our new model supports interpretations and modelling by Clairmont et al (2021) and Kroeger et al (2022).…”

“…Additionally, work by Clairmont et al (2021) used seismic attribute analysis on similar Pegasus Basin anticlinal‐synclinal couplets discussed in this study. The use of seismic attributes that measure anelastic seismic energy attenuation showed a possible absence of accumulated free gas within synclines at the depth of the BSR.…”

Assessment of spectral attributes in identifying gas hydrates in seismic data from the Pegasus Basin, offshore New Zealand

“…The absence of BSRs can be due to lack of migrating gas in the system, failure of the hydrates to trap free gas, or depending on lithology, the impedance contrast between the hydrate filled sediments and non-hydrate filled sediments may not be large enough for a seismic reflector (Bedle, 2019). Weak, discontinuous, or absent BSRs complicate the process of identifying hydrates and require implementing other methods to image the extent of the gas hydrates (Chenin and Bedle, 2020;Clairmont et al, 2021). Therefore, relying on clear BSRs alone in seismic data is unreliable (Holbrook et al, 1996), and other methods are often needed to confirm the existence of gas hydrates within the GHSZ.…”

“…These statistical attributes Li et al applied are called "shape attributes" and measure the shape of the seismic amplitude spectrum. Although these spectral attributes have not previously been applied to measure attenuation within hydrates, instantaneous and AVO attributes have proven useful for evaluation of gas hydrate-bearing sediment (Wang et al, submitted 2023;Clairmont et al, 2021), and are incorporated with skewness and kurtosis into unsupervised machine learning to further identify gas hydrates in seismic data in the absence of BSRs or other clear direct hydrocarbon indicators (DHIs).…”