Mapping of elastic properties of gas hydrates in the Carolina trough by waveform inversion

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 735-744 ◽  
Author(s):  
Ganyuan Xia ◽  
Mrinal K. Sen ◽  
Paul L. Stoffa
Keyword(s):  
South Carolina ◽  
Gas Hydrates ◽  
Waveform Inversion ◽  
Optimization Method ◽  
Frequency Component ◽  
Low Velocity ◽  
Seismic Line ◽  
Sea Floor ◽  
Free Gas ◽  
Seismic Waveform

Gas hydrates are frozen methane gas that forms at appropriate pressure and temperature conditions. They are found in the marine sediments along continental margins worldwide. They have the economic potential of being tapped as a fuel source and also have the potential as a “greenhouse” agent after being freed into the atmosphere. In seismic sections, the occurrence of the base of gas hydrates, in some areas is often marked by a bright amplitude reflection. Such reflections follow the sea floor topography and are called bottom‐simulating reflectors (BSR). The BSRs have negative polarity with respect to the sea‐floor reflection and, in a common shot or a CDP gather, the amplitude increases with offset. The negative impedance contrast causing BSRs may be due to negative velocity contrast between hydrated sediments and normal sediment below or due to the presence of free gas at the base of the hydrates. In this paper, we carry out a prestack seismic waveform inversion of multichannel seismic data collected in the offshore of South Carolina to investigate the origin of the BSRs. We apply a multistage seismic waveform inversion for this purpose. A nonlinear optimization method is applied to estimate the low‐frequency component of the velocity, whereas an amplitude‐variation‐with‐offset inversion is applied to determine high‐frequency components of the velocity field. Our detailed seismic waveform inversion along the seismic line results in at least three low‐velocity zones where the velocity is well below the velocity of the normal sediments. Such low‐velocity zones correlate very well with negative fluid factors indicating the presence of free gas. Thus we conclude that the BSR is caused by free gas at the base of the hydrates in this region. We identify at least two other layers of free gas beneath the hydrates. The thickness of each of these layers is below the resolution of the source wavelet. Our results confirm similar findings reported from Ocean Drilling Program drilling and vertical‐seismic‐profiling analysis in the same general area.

Characterising Gas Hydrate Deposits on New Zealand's Southern Hikurangi Margin using Seismic Reflection Data

2021 ◽  
Author(s):  
◽  
Hanyan Wang
Keyword(s):  
Gas Hydrates ◽  
Gas Hydrate ◽  
Research Area ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Free Gas ◽  
Reflection Data ◽  
Geological Conditions ◽  
Gas Hydrate Deposits ◽  
Seismic Lines

<p>Reprocessed Bruin 2D seismic data (recorded in 2006) from New Zealand Hikurangi Margin are presented and analyzed to show the presence of gas hydrates. We choose six seismic lines that each showed bottom-simulating reflections (BSRs) that are important indicators for the presence of gas hydrate. The aim is to obtain a higher resolution image of the shallow subsurface structures and determine the nature of the gas hydrate system in this area.  To further investigate the presence of Gas Hydrates was undertaken. There is a strong correlation between anomalous velocities and the depths of BSRs, which supports the presence of gas hydrates in the research area and is useful for detecting areas of both free gas and gas hydrate along the seismic lines.  The combination of high-resolution seismic imaging and velocity analysis is the key method for showing the distribution of gas hydrates and gas pockets in our research area. The results indicate that the distribution of both free gas and gas hydrate is strongly localized. The Discussion Chapter gives several concentrated gas hydrate deposits in the research area. Idealized scenarios for the formation of the gas hydrates are proposed. In terms of identifying concentrated gas hydrate deposits we propose the identification of the following key seismic attributes: 1) existence of BSRs, 2) strong reflections above BSRs in the gas hydrate stability zone, 3) enhanced reflections related to free gas below BSRs, 4) appropriate velocity anomalies (i.e. low velocity zones beneath BSRs and localized high-velocity zones above BSRs).  This study contributes to the understanding of the geological conditions and processes that drives the deposition of concentrated gas hydrate deposits on this part of the Hikurangi Margin.</p>

A theory-guided deep-learning formulation and optimization of seismic waveform inversion

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. R87-R99 ◽  
Author(s):  
Jian Sun ◽  
Zhan Niu ◽  
Kristopher A. Innanen ◽  
Junxiao Li ◽  
Daniel O. Trad
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Degrees Of Freedom ◽  
Data Science ◽  
Waveform Inversion ◽  
Optimization Method ◽  
Learning Rate ◽  
Physical Models ◽  
Seismic Waveform ◽  
Set Up

Deep-learning techniques appear to be poised to play very important roles in our processing flows for inversion and interpretation of seismic data. The most successful seismic applications of these complex pattern-identifying networks will, presumably, be those that also leverage the deterministic physical models on which we normally base our seismic interpretations. If this is true, algorithms belonging to theory-guided data science, whose aim is roughly this, will have particular applicability in our field. We have developed a theory-designed recurrent neural network (RNN) that allows single- and multidimensional scalar acoustic seismic forward-modeling problems to be set up in terms of its forward propagation. We find that training such a network and updating its weights using measured seismic data then amounts to a solution of the seismic inverse problem and is equivalent to gradient-based seismic full-waveform inversion (FWI). By refining these RNNs in terms of optimization method and learning rate, comparisons are made between standard deep-learning optimization and nonlinear conjugate gradient and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimized algorithms. Our numerical analysis indicates that adaptive moment (or Adam) optimization with a learning rate set to match the magnitudes of standard FWI updates appears to produce the most stable and well-behaved waveform inversion results, which is reconfirmed by a multidimensional 2D Marmousi experiment. Future waveform RNNs, with additional degrees of freedom, may allow optimal wave propagation rules to be solved for at the same time as medium properties, reducing modeling errors.

Characterising Gas Hydrate Deposits on New Zealand's Southern Hikurangi Margin using Seismic Reflection Data

2021 ◽  
Author(s):  
◽  
Hanyan Wang
Keyword(s):  
Gas Hydrates ◽  
Gas Hydrate ◽  
Research Area ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Free Gas ◽  
Reflection Data ◽  
Geological Conditions ◽  
Gas Hydrate Deposits ◽  
Seismic Lines

<p>Reprocessed Bruin 2D seismic data (recorded in 2006) from New Zealand Hikurangi Margin are presented and analyzed to show the presence of gas hydrates. We choose six seismic lines that each showed bottom-simulating reflections (BSRs) that are important indicators for the presence of gas hydrate. The aim is to obtain a higher resolution image of the shallow subsurface structures and determine the nature of the gas hydrate system in this area.  To further investigate the presence of Gas Hydrates was undertaken. There is a strong correlation between anomalous velocities and the depths of BSRs, which supports the presence of gas hydrates in the research area and is useful for detecting areas of both free gas and gas hydrate along the seismic lines.  The combination of high-resolution seismic imaging and velocity analysis is the key method for showing the distribution of gas hydrates and gas pockets in our research area. The results indicate that the distribution of both free gas and gas hydrate is strongly localized. The Discussion Chapter gives several concentrated gas hydrate deposits in the research area. Idealized scenarios for the formation of the gas hydrates are proposed. In terms of identifying concentrated gas hydrate deposits we propose the identification of the following key seismic attributes: 1) existence of BSRs, 2) strong reflections above BSRs in the gas hydrate stability zone, 3) enhanced reflections related to free gas below BSRs, 4) appropriate velocity anomalies (i.e. low velocity zones beneath BSRs and localized high-velocity zones above BSRs).  This study contributes to the understanding of the geological conditions and processes that drives the deposition of concentrated gas hydrate deposits on this part of the Hikurangi Margin.</p>

Geophysical case history of the Two Hills Colony gas field of Alberta

Geophysics ◽  
1985 ◽  
Vol 50 (7) ◽  
pp. 1061-1076 ◽  
Author(s):  
G. W. Focht ◽  
F. E. Baker
Keyword(s):  
Colony Formation ◽  
Oil And Gas ◽  
Direct Detection ◽  
Waveform Analysis ◽  
Bright Spot ◽  
Low Velocity ◽  
Gas Field ◽  
Seismic Line ◽  
Seismic Waveform ◽  
Bright Spots

Seismic waveform changes, which in their most obvious form are known as “bright spots,” have been known for some years to give direct indications of hydrocarbons. An example of successful application of waveform analysis and direct detection of gas in a shallow Lower Cretaceous formation of east‐central Alberta, Canada, is detailed. At a depth of approximately 1 800 ft, the Colony formation typically consists of only thin (10 ft) blanket sands interbedded with shale. However, in 1976, Hudson’s Bay Oil and Gas Company Ltd., encountered a 100 ft thick occurrence of channel sand (with substantial gas pay) in this formation. After some hit and miss attempts at extending the channel trend through geologic interpretation, seismic methods were applied. A seismic line over the channel well revealed a classic bright spot. Several other lines also showed bright spots in the Colony zone. The conclusions from seismic modeling are as follows. Gas within the Colony sand is seismically detectable. The relatively low velocity of the gas sand, combined with the lateral consistency of the sediments above the Colony formation, permits detection. However, the inconsistency and complexity of sediments underlying the Colony resulted in interference patterns that prevented exact quantitative analysis of gas pays. Furthermore, other geologic phenomena provided waveform changes similar to that of gas sand. Through detailed examination of the geology and evaluation of the alternative explanations of the waveform changes, successful interpretation was accomplished. Estimations of net gas pay were generally accurate within 20 percent. In some areas, very subtle anomalies in wave character representing gas pays as thin as 5 ft can now be interpreted with confidence. Several examples are given of successful detection and prediction of gas. To date (October, 1979) seismic waveform analysis has led to the drilling of 86 wells; 67 of these are commercial gas wells in the Colony formation, representing a success ratio of 78 percent. Total reserves discovered geophysically (by Hudson’s Bay Oil and Gas Co. Ltd.) to date in the Colony formation are estimated at 110 Bcf.

Nature of the low velocity zone in Cascadia from receiver function waveform inversion

2012 ◽  
Vol 337-338 ◽  
pp. 25-38 ◽  
Author(s):  
Ralf T.J. Hansen ◽  
Michael G. Bostock ◽  
Nikolas I. Christensen
Keyword(s):  
Receiver Function ◽  
Waveform Inversion ◽  
Low Velocity ◽  
Low Velocity Zone ◽  
Velocity Zone
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