A METHOD TO LOCATE AND QUANTIFY GAS HYDRATE USING LONG-OFFSET SEISMIC

Jianchun Dai*Earth Model Building Center of Excellence

Schlumberger-WesternGeco10001 Richmond Avenue, Houston, Texas 77042

USA

ABSTRACT

A method to locate and quantify gas hydrate is developed, using long-offset reflection seismic information. The method follows four basic steps: (1) Models elastic behavior of the shallow hydrate-bearing sediments, (2) Builds critical angle models as a function of burial depth or time with varying gas hydrate saturations, (3) Defines critical angle profile from pre-stack seismic gather within the gas hydrate stability zone (GHSZ), and (4) Identify and quantify gas hydrate concentration in the porous space using the critical angle information as defined in step three and the critical angle model as built in step two. The gas hydrate concentration estimation in step four can be done directly through interpolation or statistically using Bayesian-type of inversion.

Due to the fact that large changes in both amplitude and phase occur at critical and super critical angles of seismic gathers, this method is of high fidelity, robustness, and computation efficiency. It is also of extremely high resolution. Because the method does not involve AVO inversion as most conventional methods do, it avoids the uncertainties associated with AVO inversion.

The concept will be demonstrated using a 1D synthetic gas hydrate model, with real examples from the Gulf of Mexico. The method is under a provisional patent application (61-349:086).

Keywords: gas hydrates, long-offset, critical angle

NOMENCLATUREAI: acoustic impedance or P-wave impedanceAVO: amplitude versus offsetCA: critical angleGH: gas hydrates

GHSZ: gas hydrate stability zoneGOM: Gulf of Mexico PDF: probability density functionSGH: gas hydrate concentrationVp: P-wave or compressional velocity

INTRODUCTION

Gas hydrates is an ice-like compound of natural gas, such as methane, and water formed under low temperature and high pressure. Natural gas hydrates exists extensively in the shallow

sediments of deep-water oceans and vast permafrost regions. Gigantic amount of gas hydrates was estimated by several authors in the past (Kvenvolden 1998; Kvenvolden and Barnard, 1983). Gas hydrates is considered as both a

potential cleaner source of energy and an agent for shallow (drilling) hazards. Due to these reasons,

Proceedings of the 7th International Conference on Gas Hydrates (ICGH 2011),Edinburgh, Scotland, United Kingdom, July 17-21, 2011.

gas hydrates is being extensively studied by governmental institutions, research organizations, and energy industries worldwide in recent years.

In a similar way as does for hydrocarbon exploration and exploitation, reflection seismic

method has become a major tool for gas hydrate characterization and quantification. The main approach used in seismic characterization of gas hydrate is through AVO-type of seismic elastic inversion and gas hydrate rock physics modeling

to link rock elastic property to hydrate saturation as proposed by Dai et al. (2004). Many case studies have been reported by numerous authors (Dai et al. 2008 a. b; Helgerud et al., 1999). However, large uncertainties exist in the current practice of gas hydrate quantification. These

uncertainties come from the errors of both AVO-based elastic inversion and gas hydrate rock models. AVO-based elastic inversion has been known to be a typical ill-posed problem due to low rank of the inversion kernel matrix, small angle and small property perturbation restrictions. The

method is also very sensitive to the flatness of reflection events and the fidelity of seismic amplitudes of these events. In addition, model parameter in the seismic elastic inversion is usually in the form of acoustic impedance (AI),

which is the product of P-wave velocity (Vp) and density, rather than Vp alone. This also introduces ambiguity of seismic delineation of gas hydratesdue to the coupling between Vp and density.

To avoid the large uncertainties associated with

AVO inversion, I introduce a method that uses long-offset critical angle information rather than conventional small-angle AVO to quantify gas hydrates saturation. My focus is on the location of critical angle at far offsets rather than the AVO behavior of the near offsets. As will be

demonstrated, the diagnostics for the critical angle where large amplitude and phase changes occur is much more obvious than the small angle AVO effect for gas hydrate layer(s). This method is, therefore, of high fidelity and robustness. It is also

of extremely high resolution due to far-angle refraction and virtually, not much affected by the thin-layer effect, especially for the value of crtical angle. Due to the fact that this method does not involve AVO inversion, it eliminates the uncertainties associated with AVO inversion.

METHODOLOGY

Gas hydrates estimation using long-offset seismic information includes 4 major steps: rock physics modeling of gas hydrates-bearing sediments [1]; construction of gas hydrate critical angle model [2] based on elastic property derived from step one; definition of critical angle profile within gas

hydrate stability zone (GHSZ) from pre-stack seismic gather [3]; and gas hydrates concentration estimation using gas hydrate critical angle model generated in step two and the critical angle profile derived from step three as input [4]. Alternatively,

a 3D pdf can be constructed for the critical angle model (in step three) as function of burial depth/time, gas hydrate saturation, and critical angle and the concentration can be estimated via Bayesian-type of inversion. The schematic of workflow is shown in Figure 1.

Figure 1.Conceptual model of the gas hydrates estimation using long offset information.

Gas hydrate rock physics modeling

The gas hydrate-bearing rock modeling applies an effective medium theory (EMT, Dvorkin and Nur, 1996) that treats gas hydrates as load-bearing

matrix. As a result, the introduction of gas hydrates will reduce the porosity space of the shallow sediments and enhance both stiffness and rigidity of the shallow rocks. EMT was first used for gas hydrates-bearing sediments by Helgerud et al. in 1998. A comprehensive evaluation of EMT

for gas hydrates is given by Dai et al. (2004) among others. To build the elastic rock models, we first define porosity and velocity trends for brine-

filled shale and sands as a function of burial depth/time. We then replace the porosity with gas hydrates and estimate the corresponding velocities using EMT. An example gas hydrate elastic model is shown in Figure 2. The thick green curves on both panels show the P-wave velocity (Vp) trend

for shale with burial time starting from mudline to 2000 ms in time. The group of colored curves shows the Vp for hydrate-bearing sands with hydrate saturation changing from 0 to 100% of the pore space (from left to right in left panel) and

hydrate-bearing shale, with hydrate saturation changing from 0 to 100% of the pore space (from left to right in right panel). Note, for the hydrate-bearing shale case as shown in the right panel, the 0% line is identical to the thick green background shale line.

Figure 2. Vp models of gas hydrate-bearing sediments. The thick green lines on both panels are

the Vp for the background shale. The group of color lines show the Vp for gas hydrate-bearing

sands (left) and Vp for gas hydrate-bearing shale (right), with hydrate saturation changing from 0 to

100% of porous space from left to right.

Construction of gas hydrate critical angle model

The elastic models (Vp) for sand and shale sequences with hydrate-bearing sand and hydrate-

bearing shale are shown in Figure 2. Based on these velocity models, critical angle can be calculated at each depth/time for different hydrate saturations, using Snell’s law through equations 1 and 2.

2

1

2

1

sin

sin

Vp

Vp

(1)

2

1sin VpVp

c (2)

where Vp1 and Vp2 are the P-wave velocities ofthe upper layer and lower layer of an interface respectively. If Vp2 is larger than Vp1, critical and super-critical refraction will occur when angle

of incidence reaches and exceeds c , which is the

critical angle in Equation 2. As shown in equation

2, the critical angle will decrease as the contrast of the two velocities increases.

Critical angles of variant gas hydrate saturationsfor each depth/time is calculated through equation

(2). These critical angle models are shown in Figure (3). The colored curves show the critical angles for different hydrate saturation, rangingfrom 0 to 100% of the pore space with an increment of 10% from right to left in both panels.Overall, the critical angle increases with burial

depth/time for the same amount of hydrate concentration for both cases due to the rapid compaction effect of the background shale with burial depth/time. At same burial depth/time, the critical angle decreases as saturation increases.

Figure 3. Gas hydrate critical angle models. Left panel shows the critical angle variation with burial

time and hydrate saturation for hydrate-bearing sand in a shale background, and the right panel, hydrate-bearing shale in a shale background. In

both panels, hydrate saturations increase from 0 to 100% with an increment of 10 from right to left.

Critical angle model pdf generation

This step is needed if Bayes rule is used in the hydrate saturation mapping as will be discussed in step four. The pdf of the critical angle model can

be constructed as functions of burial depth/time, hydrate saturation, and corresponding critical angle using the critical angle model as sampling basis. The pdf constitutes the likelihood function in Bayesian type of mapping.

Critical angle profile definition from prestack seismic gather

As dictated by Snell’s law, super-critical refraction will occur at the surface of harder layers in

shallow sediments. The critical angle will decrease as the contrast of the hard layer velocity to the background velocity increases. Large amplitude and phase changes at critical and super-critical angles of the pre-stack seismic amplitude are the major diagnostics for refraction events. Due to

large velocity increase of hydrate-bearing sediments relative to the background in the shallow sediments, super-critical refractions occur as a result of the presence of gas hydrates in the system. By recognizing the extreme amplitude and phase gradient anomalies from pre-stack

seismic gather, critical angle profile for the gather can be defined.

Figure 4 shows an example of synthetic primary p-wave seismic gather (right panel) based on a gas

hydrate model with multiple hydrate-bearing sand layers in a shale background (left panel). A Ricker wavelet with a dominant frequency of 30 hz is used in synthetic modeling. It is very obvious that large amplitude and phase changes occur at the critical and super-critical angles of the hydrate-

bearing sand events due to the combined effect of the changes in lithology and hydrate saturation. The critical angle definition is shown by the red circles in figure 5.

Figure 4. A model of multiple gas hydrate-bearing sand layers in a shale background (left panel) and

the corresponding synthetic seismic gather (right panel, the angle of incidence varies from 0 to 80

degrees with a increment of 2 degrees.

Figure 5. Synthetic seismic gather shown in the right panel of figure 4 overlay with critical angles.

Red circles denote the critical angles

corresponding each sand layers

Gas hydrate concentration estimation

Once the gas hydrate critical angle model from

step two with optional corresponding pdf volume is defined, and critical angle profile from the seismic is picked, hydrate saturations can be mapped directly via interpolation or through Bayesian-type inversion. A critical angle model for sandy hydrate is shown in the left panel of

Figure 6. The red circles are the critical angles picked in Figure 5. As each of the colored lines represents an iso-hydrate-saturation trend from 0 to 100% from right to left, hydrate saturations of the picked events (red circles) can be readily estimated. Figure 7 shows the comparison between

the modeled ‘true’ (blue curve in panel 5) and estimated (red circles in panel 5) hydrate saturations.

For Bayesian-type of inversion, the posterior

probability of a gas hydrate-bearing sand with a certain saturation (sgh) for a given critical angle

(ca) at depth/time z, )|,( casghzp , is calculated

through Bayesian analysis expressed in equation (3).

)(/),|(*),()|,( capsghzcapsghzpcasghzp (3)

where ),|( sghzcap is the likelihood pdf function

constructed from the critical angle model as

mentioned in the previous section, ),( sghzp is

the prior information, and )(cap is the summation

of the probability for all the classes. The final sgh will be defined by following the maximum a posteriori rule (map).

Figure 6. Gas hydrate critical models as shown in Figure 3 overlay with the defined critical angles in

red circles.

Figure 7. Gas hydrate model with brine-shale and brine-sand trends overlay. Hydrate saturation in

the sand layers are shown in blue of the most right

panel. The red dots are the inverted hydrate saturations for these layers using the presented

methods.

REAL DATA EXAMPLE

An example gather from Alaminos Canyon Block 818 (AC818) in GoM is shown in figure 8. A refraction event is recognized at 4160 ms level at offset trace 32. The estimated time below seafloor is ~470 ms with a critical angle of ~42 degrees.

The estimated gas hydrate saturation is ~70% as indicated from the red star in Figure 9. A near-by well drilled through the event, showing extremely high P-wave velocity and resistivity of the interval, with estimated hydrate concentration ~ over 80% based on the log measurements.

Figure 8. An example gather showing a refraction event ~4160ms at trace ~32.

Figure 9. Red star shows the hydrate event at time

~470 with a critical angle ~42.

DISCUSSION

A single interface model is presented in figure 10, where Vp1, Vs1, and Rho1 are the elastic property

for the upper space and Vp2, Vs2, and Rho2, the property of the lower space. The real part, absolute value, and phase of Rpp are shown in blue, green and red correspondingly. Note large amplitude and phase changes occur at critical and super critical

angles. The modeled property is typical of shallow sediment (upper space) and high-concentration gas hydrate-bearing sediments (lower space).

Four models with hydrate thickness varying from 5ms, 15ms, 50ms, and 100ms (interface) are created to study the resolution of the current method, and is shown in figure 11. A Ricker wavelet with a dominated frequency of 30 hz is used for the modeling. Note virtually consistent

critical angles are shown in the modeled responses as shown in figure 12. This shows the method is not much affected by the thin layer effect, revealing the potential of very high resolution.

Figure 10. A single interface gas hydrate model: Vp1, Vs1 and Rho1 are the overburden; Vp2, Vs2, and Rho2 are the hydrate-bearing sediments. The

real, absolute, and phase of Rpp are shown in blue, green and red curves. Large amplitude and phase

changes occur at critical and super-critical angles.

Figure 11. Four single hydrate-bearing layer models (shown in red) with thickness changing

from 100ms (interface), 5ms, 15ms, and 50ms.

Figure 12. Angle gather responses of the models shown in figure 11. The angle ranges from 0 to 88

degrees with a increase of 2 degrees. The red circles are the critical angle of the models,

showing not affected by the layer thickness.

SUMMARY

Quantifying gas hydrate using long offset (criticalangle) seismic is not only efficient in terms of calculation but also robust. The method is of high fidelity and resolution due to large amplitude and phase changes at critical and super-critical angles. The magnitude of critical angle is a simple and definitive indication of the P-wave velocity contrast of a fast layer with the overlaying lithology. Observed velocity contrast is the most significant elastic indicator of the gas hydrates in shallow sediments. Given the abundance of the long offset seismic data relative to the shallow sediments, the method has high potential for extensive application.

ACKNOWLEDGEMENT

The author would like to thank Niranjan Banik, Mark Egan, and Mita Sengupta for technical review.

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