Estimation of the gas-hydrate resource volume in a small area of the Ulleung Basin, East Sea using seismic inversion and multi-attribute transform techniques

Marine and Petroleum Geology 47 (2013) 291e302

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Estimation of the gas-hydrate resource volume in a small area of the Ulleung Basin, East Sea using seismic inversion and multi-attribute transform techniques Gwang H. Lee a, *, Bo Y. Yi b, Dong G. Yoo b, Byong J. Ryu b, Han J. Kim c a b c

Department of Energy Resources Engineering, Pukyong National University, Busan 608-737, Republic of Korea Korea Institute of Geoscience and Mineral Resources, Daejon 305-350, Republic of Korea Korea Institute of Ocean Science and Technology, Ansan 426-744, Republic of Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 September 2012 Received in revised form 29 March 2013 Accepted 1 April 2013 Available online 10 April 2013

We estimated the volume of gas-hydrate and in-place gas in a small (37 km
58 km) area of the Ulleung Basin, East Sea from a dense grid of 2-D seismic and logging-while-drilling (LWD) data, using seismic inversion and multi-attribute transform techniques. Multi-attribute transform finds the relationship between any measured or calculated logs and the combination of the seismic attributes and the acoustic impedance computed from inversion. We assumed that the bottom-simulating reflector marks the base of the gas-hydrate stability zone (GHSZ). First, the pore-space gas-hydrate saturation at the wells was estimated from the simplified three-phase Biot-type equation. Then, the porosity and the pore-space gas-hydrate saturation along the seismic lines were predicted from multi-attribute transform. The GHSZ was divided into ten layers of the equal time thickness. The time thickness of each layer was converted into depth, using the timeedepth relationship constructed from seismic-to-well tie at the wells, and gridded at 500-m cell size. The average porosity and pore-space gas-hydrate saturation were computed for each layer and multiplied to obtain the average total gas-hydrate saturation which was gridded with the same cell size as the thickness grid. Thus, each 2-D cell is represented by a rock volume and an average total gas-hydrate saturation. The gas-hydrate volume for each cell was computed by the multiplication of the cell rock volume and the average total gas-hydrate saturation. Finally, the total gashydrate volume was computed by summing the gas-hydrate volumes of all cells. The estimated gashydrate and in-place gas volumes in the study area are about 3.43
109 m3 (1.21
1011 ft3) and about 4.50
1011 m3 (1.59
1013 ft3), respectively. The more conservative estimates, excluding the top three layers that comprise about 50 m of the near-seafloor sediments where the LWD data are often unreliable, are 3.03
109 m3 (1.07
1011 ft3) for gas hydrate and about 3.97
1011 m3 (1.40
1013 ft3) for gas. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Gas hydrate Resource Inversion Multi-attribute transform

1. Introduction Gas hydrates are naturally occurring ice-like crystalline solids, composed of a lattice of hydrogen-bonded water molecules with voids occupied by gas molecules (mostly methane). The gashydrate stability zone (GHSZ) is the depth interval from the seafloor below about 500 m of water depth or from the ground surface in permafrost regions to a certain depth with the fixed range of temperature and pressure where gas hydrates form and remain stable. The early model for gas-hydrate distribution in the 1980s

* Corresponding author. Tel.: þ82 51 629 6558; fax: þ82 51 629 6553. E-mail addresses: [email protected], [email protected] (G.H. Lee). 0264-8172/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpetgeo.2013.04.001

was simple, due to the scarcity of field data, depicting gas hydrates as almost uniformly distributed components in the GHSZ. With growing knowledge of gas-hydrate distribution, the occurrence of gas hydrate in the GHSZ is now known to be very complex with significant lateral and vertical variability (Boswell and Collett, 2006). The complex interaction of many geologic factors, including temperature, pressure, gas chemistry, salinity, availability of gas and water, fluid migration pathways, and lithology, controls the gas-hydrate occurrence (Collett, 1995; Boswell and Collett, 2006). The amount of gas trapped in the global gas-hydrate accumulations is potentially enormous but estimates, based mostly on the early uniform model, are highly speculative, ranging more than three orders of magnitude from about 2.8
1015 to 8
1018 m3 (w9.9
1016 to 2.8
1020 ft3) (Kvenvolden, 1988,

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1993; Milkov, 2004; Collett et al., 2009). Estimates of gas resources in much smaller but regional-scale gas-hydrate accumulations (e.g., Holbrook et al., 1996; Dickens et al., 1997) are also quite uncertain as they are based on simple extrapolation or single average values for various parameters for the entire area.

This uncertainty can be significantly reduced if a dense grid of 2-D seismic data or preferably 3-D seismic data and well-log data are available. Numerous studies have employed seismic reflection and welllog data to identify and predict the in-situ concentration of

Figure 1. (A) Physiographic map of East Sea (Japan Sea). Box represents Ulleung basin shown in (B). (B) Bathymetry of Ulleung Basin and study area with locations of seismic reflection data and wells. Heavy line and respective figure numbers indicate seismic profiles shown in other figures. Contours in meters.

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various gas-hydrate accumulations. McConnell and Kendall (2004) mapped high acoustic impedance (the product of density and Pwave velocity) zones, computed from inversion, in northwest Walker Ridge in the Gulf of Mexico that were interpreted to be gas-hydrate deposits updip from the trapped gas. Acoustic impedance data from inversion studies near the Mallik research wells, Mackenzie Delta, Canada were also used to determine the extent of gas-hydrate occurrence that had not been previously mapped (Bellefleur et al., 2006). Dai et al. (2008) made pre-drill estimates of gas-hydrate saturations in the Keathley Canyon area in the northern Gulf of Mexico from 3-D pre-stack inversion and rock-property models. Their pre-drill estimates at the well location agree with the gas-hydrate occurrence and saturations estimated from the well-log data (Boswell et al., 2009; Shelander et al., 2010). Shelander et al. (2012) showed that gas-hydrate saturations in the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II area, predicted from pre-stack inversion, are comparable to those calculated from well-log data especially in areas of moderate to high concentrations of gas hydrates. Riedel and Shankar (2012) applied effective medium modeling to the

293

acoustic impedance to estimate the gas-hydrate concentrations around the wells drilled in the Krishna Godavari basin off eastern India and further proposed the running-sum of the seismic similarity attribute to define confidence limits for extrapolation of well-log data. Multi-attribute transform provides the link between seismic attributes, including the acoustic impedance from seismic inversion, and well-log data and help predict any measured or calculated well-log properties away from well control (Schultz et al., 1994; Russell et al., 1997). Thus, there is great value to industry and academia in applying seismic inversion and multi-attribute transform to characterize and quantify gas-hydrate accumulations between wells. However, to date, there are few instances of using these techniques directly to make estimates of gas-hydrate resources and gas trapped in gas hydrates. In this study, we used 2-D post-stack seismic inversion and multi-attribute transform to estimate the resource volumes of gas hydrates and their gas in a small (37 km
58 km) area of the northern central part of the Ulleung Basin, East Sea (Japan Sea) at water depths of about 2000 me2200 m (Fig. 1). The drilling

WELL DATA

INVERSION MULTI-ATTRIBUTE TRANSFORM

SEISMIC DATA

POROSITY

PORE-SPACE GAS-HYDRATE SATURATION

MULTIPLICATION

TIME-DEPTH CONVERSION AND GRIDDING

GRIDDING

ROCK-VOLUME GRID (10 LAYERS WITH 500 m x 500 m CELLS)

MULTIPLICATION

GAS-HYDRATE RESOURCE VOLUME

Figure 2. Data analysis workflow. Porosity and pore-space gas-hydrate transform. Gas-hydrate stability zone was divided into ten layers and average pore-space gas-hydrate saturation, was gridded with same cell volume for each cell. Summation of total gas-hydrate saturation for all resource volume.

TOTAL GAS-HYDRATE SATURATION

TOTAL GAS-HYDRATE SATURATION GRID (10 LAYERS WITH 500 m x 500 m CELLS)

GAS RESOURCE VOLUME

saturation were predicted from seismic and well-log data by performing inversion and multi-attribute gridded. Average total gas-hydrate saturation, computed from multiplication of average porosity and size. Multiplication of average total gas-hydrate saturation and cell rock volume gave total gas-hydrate cells is gas-hydrate resource volume in study area. Gas-hydrate resource volume was converted to gas

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expeditions in the Ulleung Basin in 2007 and 2010 recovered gas hydrates at water depths between about 900 m and 2200 m. The gas hydrates in the Ulleung Basin occur as pore filling (or matrix or pore-filling matrix) in sand or volcanic ash layers, or as fracturefilling veins and nodules in pelagic/hemipelagic mud (Lee, 2011). Up to 150 m of gas hydrate-bearing reservoirs were documented at the well that penetrated a columnar zone of low seismic reflectivities or wipe-out (Park et al., 2008). Rich geological and geophysical data from the two drilling expeditions make the Ulleung Basin an excellent place to apply seismic inversion and multi-attribute transform for the estimation of gas-hydrate and gas resources. Our assessment method can be applied to much larger, regional-scale estimation of gas-hydrate and gas resources if seismic data coverage is reasonably complete and key well-log data are available. 2. Geologic setting The East Sea is a back-arc basin behind the Japanese Island arc (Fig. 1A). Extension that began in the Early Oligocene formed three deep basins in the East Sea: the Japan, Yamato, and Ulleung basins, separated by rifted continental fragments (Tamaki et al., 1992). The regional plate reorganization in the Middle Miocene led to the cessation of spreading in the East Sea (Sibuet et al., 2002; Lee et al., 2011) and initiated back-closure. The back-arc closure caused deformation (e.g., thrusts, anticlines, folding and faulting) of the peripheral regions of the East Sea, including the southern margin of the Ulleung Basin (Ingle, 1992). The Ulleung Basin is bounded by the steep continental slope of the Korean Peninsula to the west and by the rugged Korea Plateau to the north. The gentle slopes of the Oki Bank and the Japanese islands form the eastern and southeastern margins of the basin. The basin floor is fairly smooth and dips gently to the northeast. During the latest Neogene, margin-wide slope failures, caused by the regional deformation related to the back-arc closure, resulted in basinwide deposition of massetransport complexes, consisting dominantly of debris-flow deposits (Lee and Suk, 1998). Since the Pleistocene, massetransport complexes have retreated rapidly landward, forming debris aprons along near the base-of-slope region, while turbidite and hemipelagic sedimentation has prevailed in the central basin (Lee and Suk, 1998). 3. Data Data used in this study include: (1) about 1100 km of 60-fold, stacked seismic reflection profiles provided by the Korea Institute of Geoscience and Mineral Resources (KIGAM) and (2) loggingwhile-drilling (LWD) data (sonic, density, and density-porosity logs) from three Second Gas Hydrate Drilling Expedition (UBGH2) wells (UBGH2-2_2A, UBGH2-9A, and UBGH2-10A) drilled in 2010 (Fig. 1B). The seismic data consist of 10 EW lines and 12 NS lines; the EW lines are separated by about 3 km and the NS lines by about 5e7 km. The seismic data were acquired by KIGAM on RV Tamhae II in 2005. A 240-channel (3000-m long) streamer recorded shots from a 1035-in3 (2000 psi) six air-gun array. The hydrophone group interval and shot spacing were 12.5 m and 25 m, respectively. The sampling interval is 1 ms. The data were processed at KIGAM; true amplitude was preserved during processing. 4. Data analysis workflow and results We used Kingdom (version 8.6) for seismic data interpretation and analysis and Hampson-Russell (version CE8R4) for inversion

and multi-attribute transform. The gas-hydrate and gas resources were estimated deterministically from a simple multi-layer rock volume model of porosity and pore-space gas-hydrate saturation. Figure 2 shows the data analysis workflow.

4.1. Seismic inversion Post-stack seismic inversion transforms seismic data, which is the wavelet response at an interface, into acoustic impedance, which provides layer information. The data input to post-stack seismic inversion typically consist of seismic data, sonic and density logs, and a set of interpreted horizons. In this study, we applied the model-based inversion algorithm which is most widely used in both post-stack and pre-stack inversions to compute the absolute impedance. Model-based inversion requires the estimation of source wavelet that is convolved with the well reflectivity series, computed from log impedance, to generate the synthetic traces. First, the amplitude spectrum of the wavelet was derived statistically from the seismic data. Then, a series of constant phase rotations was applied to this statistically-extracted wavelet. The synthetic trace was constructed for each rotation and correlated with the seismic trace at the wells. Finally, the phase rotation that produces the maximum correlation was selected for the phase of the wavelet. The synthetic-to-seismic tie

TWO-WAY TRAVEL TIME (sec.) FROM SEAFLOOR

0

0

0.1

0.2

0.3

0.4

UBGH2-2_2A UBGH2-9A UBGH2-10A FIT CURVE DEPTH (meters) FROM SEAFLOOR

294

100

200

300 DEPTH = 103.0 x TIME2 + 744.5 x TIME – 1 Figure 3. Time-depth curves for three wells and fit curve used for time-depth conversion.

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was further fine-tuned to optimize the timeedepth relationship in the area (Fig. 3). In model-based inversion, an initial impedance model is constructed from the interpolation of the low-frequency components of the impedance logs from the wells guided by the interpreted horizons. The initial model supplies the low frequencies missing from seismic data to get the absolute impedance value and also prevents high-frequency details in the model from influencing the final inversion result (Hampson-Russell, 2007a). The initial model is perturbed iteratively until the resulting synthetic traces match the real traces within some tolerance level. We used the seafloor and the bottom-simulating reflector (BSR) as the guide horizons. Because the BSR in our data is only locally recognizable, the approximate and smoothed BSR mapped over the entire basinfloor area of the Ulleung Basin by Horozal et al. (2009) was used. The higher number of guide horizons may result in the higher accuracy of the inversion results. However, the debris-flow deposits make it very difficult to interpret horizons in the GHSZ in the study area. Furthermore, because the base of the GHSZ is not a lithologic boundary, it may not be optimal for a guide horizon. Nevertheless, because model-based inversion updates the synthetic traces until they closely match the real traces, the result based on the simple initial model with only the top and base horizons differs only by a few percent from that based on the initial model with a larger number of guide horizons (Huck et al., 2010).

ERROR = 158.206

2.7

295

Figure 4 is the result of applying model-based inversion for the UBGH2-9A well. The first panel shows an overlay of three impedance curves: the initial model in black, the original impedance in blue and the inversion result in red. The original impedance and the inversion result match very well especially in the GHSZ. The correlation between the synthetic and the real seismic traces shown in the second panel is close to 1.0. Figure 5 shows the seismic line crossing the UBGH2-9A well, the initial impedance model, and the result from model-based inversion. Although the initial model is very simple as it is merely a linear interpolation of the log impedance at the wells, the inversion result shows the variations and details very similar to those seen in the seismic profile. The impedance is particularly high immediately above the BSR which corresponds approximately to the base of the GHSZ. 4.2. Estimation of pore-space gas-hydrate saturation at the wells The pore-space gas-hydrate saturation (Fig. 6) at the wells was estimated from the sonic-log P-wave velocities, using the simplified three-phase Biot-type equation (STPBE) of Lee (2008). The STPBE is based on a pore-filling model which treats gas hydrate as a solid-phase component in gas hydrate-bearing sediments. In the pore-filling model, sediment grains, gas hydrate, and pore fluid form three homogeneous, interwoven frameworks. The STPBE is applicable to the logging or seismic data (Lee, 2008).

CORRELATION = 0.991884

ORIGINAL LOG INITIAL MODEL INVERSION

SEAFLOOR 2.9

3.0 ERROR CALCULATION WINDOW

TWO-WAY TRAVEL TIME (sec.)

WAVELET

2.8

3.1

3.2

BSR

IMPEDANCE

SYNTHETHIC & SEISMIC

ERROR

Figure 4. Result of inversion at UBGH2-9A well. Left panel: Initial low-frequency model impedance in black, original log impedance in blue, and inversion result in red. Middle panel: Synthetic traces in red and real seismic traces in black. Same trace is repeated five times. Right panel: Error traces (real minus synthetic). Same error trace is repeated five times. Original impedance and inversion result match very closely. Correlation between synthetic and real seismic traces is close to 1.0.

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Figure 5. (A) Seismic line showing gas-hydrate stabilityzone (GHSZ) dominated by debris-flow deposits and turbidite/hemipelagic sediments. Debris-flow deposits are characterized by structureless-to-chaotic internal seismic facies and turbidite/hemipelagic sediments by continuous reflections. Bottom-simulating reflector (BSR), corresponding to boundary between base of GHSZ and free gas below, is only locally identifiable where it crosscuts stratigraphy. Approximate and smoothed BSR, mapped by Horozal et al. (2009), was adopted. (B) Initial impedance model constructed from interpolation of low-frequency impedance logs from wells guided by seafloor and BSR. (C) Acoustic impedance computed from model-based inversion showing variations and details similar to those seen in seismic profile. Impedance is particularly high immediately above BSR. See Figure. 1 for location.

The P-wave velocity (Vp) of gas hydrate-bearing sediments is given by:

Vp ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k þ 4m=3

rb

(1)

where k and m are bulk and shear moduli of gas hydrate-bearing sediments, rb is the bulk density of gas hydrate-bearing sediments, given by rb ¼ rs(1  f) þ rwf(1  Sh) þ rhfSh, where f and

Sh are the porosity and the pore-space gas-hydrate saturation (¼fh/ f) and the subscript s, w, and h refer to sediment grain, water, and gas hydrate, respectively. The bulk (k) and shear (m) moduli of gas hydrate-bearing sediments are computed, respectively, from the following equations:

k ¼ ks ð1  b1 Þ þ b1 K * 2

m ¼ ms ð1  b2 Þ;

(2)

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297

Figure 6. Sonic, density, density-porosity, and resistivity logs and pore-space gas-hydrate saturation computed from simplified three-phase Biot-type equation (STPE) of Lee (2008).

with

1 ðb  fÞ fw fh ¼ 1 þ þ ; ks kw kh K*

b1 ¼

fas ð1 þ aÞ ; ð1 þ afas Þ

b2 ¼

fas ð1 þ gaÞ 1 þ 2a ; and g ¼ ð1 þ gafas Þ 1þa

where 4as is the apparent porosity for the sediment framework and a is the consolidation factor (Pride et al., 2004; Lee, 2005). The apparent porosity for the sediment framework (fas) is defined as fas ¼ fw þ 3 fh, with fw ¼ (1  Sh)f and fh ¼ Shf. The parameter 3, which accounts for the reduced impact of gas hydrate relative to compaction in terms of stiffening the host sediment framework (Lee and Waite, 2008), is assumed to be 0.12 as recommended by Lee (2007) and Lee and Waite (2008). The consolidation factor (a) accounts for sediment stiffening due to consolidation and using a constant value is sufficient in most cases (Lee and Waite, 2008). The consolidation factors at the three wells, estimated by fitting the Pwave velocities in gas hydrate-free zones, range from 80 to 100. We used a constant value of 90. We assumed that the sediments are composed of clay and quartz sand. The clay contents were taken from the post-drilling report of the UBGH2 (Ryu et al., 2012). The elastic moduli of the composite sediment grains were computed using Hashin and Shtrikman’s (1963) model as shown in Helgerud et al. (1999) and Carcione and Gei (2004). The elastic parameters for the sediment constituents taken from Lee and Waite (2008) are given in Table 1. The estimated pore-space gas-hydrate saturations were converted into the log property to be input to multi-attribute transform. 4.3. Multi-attribute transform: prediction of porosity and porespace gas-hydrate saturation away from well control The multi-attribute transform technique can predict rock properties beyond the well location from seismic attributes

Table 1 Elastic parameters for sediment constituents. Sediment constituents

r (g/cm)

k (GPa)

m (GPa)

Quartz Clay Gas Hydrate Water

2.65 2.58 0.91 1

36.6 20.9 6.41 2.25

45.5 6.85 2.54 0

calibrated with well-log data (Schultz et al., 1994; Russell et al., 1997; Hampson et al., 2001). It finds a linear or non-linear relationship between seismic attributes and the target log values (Hampson Russell, 2007b). The seismic attributes can be any measures of seismic data, including acoustic impedance. Acoustic impedance can better constrain the prediction than most samplebased seismic attributes because it represents sedimentary layering and any physical properties affecting the velocity and density of the sedimentary layers. The target to predict can be any measured or calculated logs such as porosity, velocity, or gashydrate saturation. The transform in the linear mode consists of an explicit combination of attributes derived by a multi-linear regression. In the non-linear mode, a neural network is trained, using selected attributes as inputs. Neural network usually predicts better and provides higher resolution (Hampson et al., 2001). In this study, the porosity and the pore-space gas-hydrate saturation were estimated from the neural network analysis. The input density-porosity logs were smoothed by moving average with a three-point window because the smoothed porosity is better correlated with seismic attributes. First, linear multiattribute analysis was performed to determine which attributes are best for predicting the porosity and the pore-space gas-hydrate saturation. Two attributes were selected for porosity: 1/ impedance and cosine instantaneous phase, and four attributes for gas-hydrate saturation: impedance2, integrated absolute amplitude, amplitude weighted frequency, and dominant frequency. Then, these two sets of attributes were used in neural network transform of the seismic data to predict the porosity and the pore-space gas-hydrate saturation, respectively. The correlations between the original and the predicted porosity and gashydrate saturation at the UBGH2-9A well are about 0.93 and about 0.97, respectively (Fig. 7A and B). The cross-validation (Fig. 7E and F), which predicts the log properties by hiding each of the wells in turn and using the remaining wells, shows a considerably lower correlation (about 0.53) for the gas-hydrate saturation. However, the overall trends of the gas-hydrate saturation from cross-validation are quite comparable to those of the original logs although the gas-hydrate saturation appears to be underpredicted. The results of multi-attribute transform for the seismic line crossing the UBGH2-9A are shown in Figure 8. The predicted porosity decreases gradually with depth and is generally lower in debris-flow deposits than in turbidite/hemipelagic sediments as reported by Riedel et al. (2012) in the area. The high (>10%)

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pore-space gas-hydrate saturation observed near the bottom of the well is extensive and the moderate saturation in the upper part of the well appears to be more local. The zone of near zero gas-hydrate saturation between these two gas hydrate-bearing intervals is predicted to continue away from the well. Overall, the predicted porosity and gas-hydrate saturation reflect the details of the seismic amplitude quite well. However, the well control in this study may not be sufficient to take full advantage of the multiattribute transform technique. Thus, it should be noted that the predicted porosity and gas-hydrate saturation can serve only as the first-order estimations. Furthermore, the LWD data for the very shallow, near-seafloor sediments are often unreliable due to drilling issues. P-wave velocities can be of inferior quality within very shallow (50e100 m) marine sediments because of the effects of leaky P-wave modes which are dispersive and can have high amplitudes (Goldberg et al., 2008). 4.4. Estimation of gas-hydrate and its gas resources We took a simple deterministic approach to estimate the total gas-hydrate volume in the area using the following equation:

Gas  hydrate volume ¼ RV$f$Sh

(3)

where RV, f, and Sh are the rock volume, the porosity, and the porespace gas-hydrate saturation, respectively. The GHSZ was divided into ten layers (L01 e L10, from bottom to top) of the equal time thickness, assuming that the BSR approximates the base of the GHSZ (Fig. 8). The ten layers are proportional, conforming to the seafloor and the BSR. The layer boundaries are referred to as H00 to H10 from bottom to top; H00 and H10 correspond to the base of the GHSZ and the seafloor, respectively. The thickness of each layer was computed, using the time-depth relationship from the well ties, and gridded with 74
116 cells at 500-m cell spacing. The layer thicknesses range from about 14 to about 19 m. The horizontal cell size is much larger than the layer thickness (i.e., the cell height) because of the assumption that geologic variations are much more rapid along the vertical direction than along directions parallel to stratigraphy. The 2-D rock-volume grid for each layer was constructed from the multiplication of the layer-thickness grid and the cell area (500 m
500 m, 250,000 m2). Therefore, the rock-volume grid for each layer consists of 74
116 cells whose volumes vary proportionally to the thickness of the layer. Then, the average porosity and pore-space gas-hydrate saturation for each layer were computed and gridded with the same cell number and size as the 2-D rock volume grid. The grid for the average total gas-hydrate saturation for each layer was constructed by the multiplication of the average porosity and the average porespace gas-hydrate saturation. We assumed that the average total gas-hydrate saturation is representative of the average property of the volume of the cell. Thus, each 2-D cell is represented by the rock volume and the average total gas-hydrate concentration. The multiplication of the rock volume grid and the average total gashydrate concentration grid for each layer gives the 2-D gashydrate volume grid for each layer (Fig. 9). The summation of the gas-hydrate volume of the ten layers is the estimate of the total gas-

299

hydrate volume (3.43
109 m3 or 1.21
1011 ft3) in the study area. The maximum in-place gas volume (about 5.62
1011 m3 or 1.98
1013 ft3) in the gas hydrates, assuming 100 percent of void occupancy, was computed by multiplying the total gas-hydrate volume by 164 since one unit volume of gas hydrate yields about 164 unit volumes of gas when dissolved at normal surface temperature and pressure (Davidson et al., 1978). Because the void occupancy by gas typically range from 70 to 95 percent (Williams et al., 2003), the final estimate of the in-place gas volume was adjusted by multiplying the maximum in-place gas volume by 0.8, which is about 4.50
1011 m3 (1.59
1013 ft3) of gas. The more conservative estimates, excluding the top three layers that are equivalent to about 50 m of the near-seafloor sediments where the LWD data are often of inferior quality, are 3.03
109 m3 (1.07
1011 ft3) for gas hydrate and about 3.97
1011 m3 (¼1.40
1013 ft3) for gas. These estimates are close to 90 percent of those from the ten layers because the gas-hydrate saturation in the very shallow sediments is generally very low. If we assume that the gas-hydrate distribution in the study area (2146 km2) is representative of the Ulleung Basin, the total gashydrate and the in-place gas resources in the basin-floor area (ca. 37,000 km2) of the Ulleung Basin with water depths greater than 1000 m are about 5.91
1010 m3 (2.09
1012 ft3) and 7.75
1012 m3 (2.74
1014 ft3), respectively, based on the estimates including the ten layers in the study area. This estimate for the total in-place gas resource in gas hydrates in the Ulleung Basin is about 0.04 percent of the most-cited estimate of gas resource (2.1
1016 m3, 7.4
1017 ft3) in the global gas-hydrate accumulations, proposed by Kvenvolden (1988). 5. Summary This study is the first attempt to estimate the gas-hydrate resource in the Ulleung Basin. The seismic inversion and multiattribute transform techniques provided the first-order estimations of the gas-hydrate and in-place gas resources in the small basin-floor area covered by the dense grid of 2-D seismic data and three wells. We applied the STPBE to compute the pore-space gashydrate saturation at the wells. The porosity and the gas-hydrate saturation away from well control were predicted from the neural-network multi-attribute transform. The predicted gashydrate saturation is high near the base of the GHSZ and decreases rapidly toward the seafloor. The GHSZ was divided into ten layers for each of which the average porosity and gas-hydrate saturation were computed. The rock volume and the average porosity and gas-hydrate saturation of each layer were gridded, multiplied, and summed to obtain the total volume of gas hydrate for the layer. The gas-hydrate volume was converted into the gas volume assuming 80 percent of void occupancy. Our estimates of the gas-hydrate and in-place gas resources in the area are about 3.43
109 m3 (1.21
1011 ft3) and about 4.50
1011 m3 (1.59
1013 ft3), respectively. Assuming that these numbers are representative of the entire basin-floor area of the Ulleung Basin, the in-place gas resource in the basin is about 7.75
1012 m3 (2.74
1014 ft3) which is about 0.04 percent of the most-cited estimate of gas resource in the global gas-hydrate accumulations.

Figure 7. Results of neural network multi-attribute transform at three wells and cross-validation. (A) Log porosity (black) and predicted porosity (red). (B) Gas-hydrate saturation computed using STPBE (black) and predicted gas-hydrate saturation (red). (C) Log vs. predicted porosity crossplot. (D) STPBE vs. predicted gas-hydrate saturation crossplot. (E) Cross-validation of predicted porosity. (F) Cross-validation of predicted gas-hydrate saturation. Correlations between original and predicted logs for porosity and gas-hydrate saturation are about 0.93 and about 0.97, respectively. Porosity was slightly overpredicted and underpredicted for ranges below and above about 70% of porosity, respectively. Gas-hydrate saturation was overpredicted at very low saturations (ca. <1%) and underpredicted at relatively high saturations (ca. > 7%). Cross-validation was performed while each of three wells was hidden in turn and porosity and gas-hydrate saturation were predicted using remaining wells. Correlation for cross-validation is considerably lower (about 0.53) for gas-hydrate saturation but overall trends are quite comparable to original logs.

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Figure 8. (A) Seismic line crossing UBGH2-9A well. Gas-hydrate stability zone (GHSZ) was divided into ten layers of equal time thickness. (B) Predicted porosity with smoothed density porosity log of UBGH2-9A well overlaid. Predicted porosity decreases gradually with depth. Porosity of seafloor and very shallow subseafloor sediments can be very high because cover of low-permeability clay prevents them from draining. (C) Predicted pore-space gas-hydrate saturation with pore-space gas-hydrate saturation log of UBGH2-9A well overlaid. High gas-hydrate saturation near bottom of well is laterally extensive whereas moderate saturation in upper part of well appears to be more local. See Figure. 1 for location.

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Figure 9. 500 m
500 m grids of gas-hydrate volume for: (A) L01, (B) L03, (C) L05, and (D) L07. Gas-hydrate volume decreases rapidly toward seafloor.

Acknowledgments

References

We thank the Gas Hydrate Research and Development Organization (GHDO) of Korea for permission to publish the results and for providing funding for this study. Partial financial support to G.H. Lee was provided by the Ministry of Land, Transport and Maritime Affairs of Korea (Construction of Carbon Storage Map and Selection of Demonstration Sites in Korean Offshore Areas). The comments and suggestions by the guest editors and two anonymous reviewers greatly improved the quality of the paper. Kingdom SuiteÒ, provided by Seismic Micro-Technology, Inc. and Hampson-RussellÒ, provided by CGGVeritas, were used for data analysis.

Bellefleur, G., Riedel, M., Brent, T., 2006. Seismic characterization and continuity analysis of gas-hydrate horizons near Mallik research wells, Mackenzie Delta, Canada. The Leading Edge 25, 599e604. Boswell, R., Collett, T.S., 2006. The Gas Hydrates Resource Pyramid. Fall 2006 Methane Hydrate Newsletter. U.S. Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory, pp. 1e4. Boswell, R., Shelander, D., Lee, M., Latham, T., Collett, T., Guerin, G., Moridis, G., Reagan, M., Goldberg, D., 2009. Occurrence of gas hydrate in Oligocene Frio sand: Alaminos Canyon Block 818: northern Gulf of Mexico. Marine and Petroleum Geology 26, 1499e1512. Carcione, J.M., Gei, D., 2004. Gas-hydrate concentration estimated from P- and Swave velocities at the Mallik 2L-38 research well, Mackenzie Delta, Canada. Journal of Applied Geophysics 56, 73e78.

302

G.H. Lee et al. / Marine and Petroleum Geology 47 (2013) 291e302

Collett, T.S., 1995. Gas hydrate resources of the United States. In: Gautier, D.L., Dolton, G.L., Takahashi, K.I., Varnes, K.L. (Eds.), 1995 National Assessment of United States Oil and Gas Resources on CD-ROM. U.S. Geological Survey Digital Data Series, vol. 30. Collett, T.S., Johnson, A., Knapp, C., Boswell, R., 2009. Natural gas hydrates e a review. In: Collett, T., Johnson, A., Knapp, C., Boswell, R. (Eds.), Natural Gas Hydrates e Energy Resource Potential and Associated Geologic Hazards. American Association of Petroleum Geologists Memoir, vol. 89, pp. 146e219. Dai, J., Snyder, F., Gillespie, D., Koesoemadinata, A., Dutta, N., 2008. Exploration for gas hydrates in the deepwater, northern Gulf of Mexico: Part I. A seismic approach based on geologic model, inversion, and rock physics principles. Marine and Petroleum Geology 25, 830e844. Davidson, D.W., El-Defrawy, M.D., Fulgem, M.O., Judge, A.S., 1978. Natural gas hydrates in northern Canada. In: Proceedings of the 3rd International Conference on Permafrost, vol. 1. National Research Council of Canada, Ontario, Canada, pp. 938e943. Dickens, G., Paull, C.K., Wallace, P., the ODP Leg 164 Scientific Party, 1997. Direct measurement of in situ methane quantities in a large gas-hydrate reservoir. Nature 385, 426e428. Goldberg, D., Guerin, G., Malinverno, A., Cook, A., 2008. Velocity analysis of LWD and wireline sonic data in hydrate-bearing sediments on the Cascade margin. In: Proceedings of the 6th International Conference on Gas Hydrate, Vancouver, Canada. Hampson, D., Schuelke, J.S., Quirein, J.A., 2001. Use of multiattribute transforms to predict log properties from seismic data. Geophysics 66, 220e236. Hampson-Russell, 2007a. STRATA Guide. Hampson-Russsell, Veritas Geophysical Company, Calgary. Hampson-Russell, 2007b. EMERGE Guide. Hampson-Russsell, Veritas Geophysical Company, Calgary. Hashin, Z., Shtrikman, S., 1963. A variational approach to the theory of the elastic behaviour of multiphase materials. Journal of the Mechanics and Physics and Solids 11, 127e140. Helgerud, M.B., Dvorkin, J., Nur, A., Sakai, A., Collett, T., 1999. Elastic-wave velocity in marine sediments with gas hydrates: effective medium modeling. Geophysical Research Letters 26, 2021e2024. Holbrook, W.S., Hoskins, H., Todd, W.T., Stephen, R.A., Lizarralde, D., the ODP Leg 164 Scientific Party, 1996. Methane hydrate and free gas on the Blake ridge from vertical seismic profiling. Science 273, 1804e1843. Horozal, S., Lee, G.H., Yi, B.Y., Yoo, D.G., Park, K.P., Lee, H.Y., Kim, W., Kim, H.J., Lee, K., 2009. Seismic indicators of gas hydrate and associated gas in the Ulleung Basin, East Sea (Japan Sea) and implications of heat flows derived from depths of the bottom-simulating reflector. Marine and Petroleum Geology 258, 126e138. Huck, A., Quiquerez, G., de Groot, P., 2010. Improving seismic inversion through detailed low frequency model building. In: 72nd EAGE Conference & Exhibition Incorporating SPE EUROPEC 2010, Barcelona, Spain. Ingle Jr., J.C., 1992. Subsidence of the Japan Sea: stratigraphic evidence from ODP sites and onshore sections. In: Tamaki, K., Suyehiro, K., Allan, J., McWilliams, M. (Eds.), Proceedings of ODP Scientific Results Scientific Results. Part 2. Ocean Drilling Program, College Station, Texas, vol. 127/128, pp. 1197e1218. Kvenvolden, K.A., 1988. Methane hydrate e a major reservoir of carbon in the shallow geosphere? Chemical Geology 71, 41e51. Kvenvolden, K.A., 1993. A primer in gas hydrates. In: Howell, D.G. (Ed.), The Future of Energy Gases: U.S. Geological Survey Professional Paper 1570, pp. 279e292. Lee, G.H., Yoon, Y.H., Nam, B., Lim, H., Kim, Y.-S., Kim, H.J., Lee, K., 2011. Structural evolution of the southwestern margin of the Ulleung Basin, East Sea (Japan Sea) and tectonic implications. Tectonophysics 502, 293e307. Lee, G.H., Suk, B., 1998. Latest NeogeneeQuaternary seismic stratigraphy of the Ulleung basin, East sea (Sea of Japan). Marine Geology 146, 205e224.

Lee, M.W., 2005. Proposed Moduli of Dry Rock and Their Application to Predicting Elastic Velocities of Sandstones. U. S. Geological Survey Scientific Investigation Report 2005-5119, p. 14. Lee, M.W., 2007. Velocities and Attenuations of Gas Hydrate-bearing Sediments. U.S. Geological Survey Scientific Investigations Report 2007-5264, p. 11. Lee, M.W., 2008. Models for Gas Hydrate-bearing Sediments Inferred from Hydraulic Permeability and Elastic Velocities. U.S. Geological Survey Scientific Investigations Report 2008-5219, p. 20. Lee, M.W., Waite, W.F., 2008. Estimating pore-space gas hydrate saturations from well log acoustic data. Geochemistry, Geophysics, Geosystems 9, 8. http:// dx.doi.org/10.1029/2008GC002081. Q07008. Lee, S.-R., Gas Hydrate R/D Organization, UBGH2 Scientific Party, 2011. 2nd Ulleung Basin Gas Hydrate Expedition (UBGH2): Findings and Implications. U.S. Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory, pp. 6e9. Methane Hydrate Newsletter 11. McConnell, D., Kendall, B., 2004. Using seismic inversion methods to characterize the extents of gas hydrate traps in northeast Walker Ridge-deepwater Gulf of Mexico. In: AAPG Hedberg Conference, Vancouver, British Columbia, Canada, p. 3. Milkov, A.V., 2004. Global estimates of hydrate-bound gas in marine sediments: how much is really out there? Earth-science Reviews 66, 183e197. Park, K.P., Bahk, J.J., Kwon, Y., Kim, G.Y., Riedel, M., Holland, M., Schultheiss, P., Rose, K., UBGH-1 Scientific Party, 2008. Korean National Program Expedition Confirms Rich Gas Hydrate Deposits in the Ulleung Basin, East Sea. U.S. Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory, pp. 6e9. Spring 2008 Methane Hydrate Newsletter. Pride, S.R., Berryman, J.G., Harris, J.M., 2004. Seismic attenuation to wave-induced flow. Journal of Geophysical Research 109 (B01201), 16. http://dx.doi.org/ 10.1029/2003JB002639. Riedel, M., Shankar, U., 2012. Combining impedance inversion and seismic similarity for robust gas hydrate concentration assessments e a case study from the KrishnaeGodavari basin, East Coast of India. Marine and Petroleum Geology 36, 35e49. Riedel, M., Bahk, J.-J., Scholz, N.A., Ryu, B.-J., Yoo, D.-G., Kim, W., Kim, G.Y., 2012. Mass-transport deposits and gas hydrate occurrences in the Ulleung Basin, East Sea e Part 2: gas hydrate content and fracture-induced anisotropy. Marine and Petroleum Geology 35, 75e90. Russell, B., Hampson, D., Schuelke, J., Quirein, J., 1997. Multiattribute seismic analysis. The Leading Edge 16, 1439e1443. Ryu, B.-J., Kim, G.-Y., Chun, J.-H., Bahk, J.Y., UBGH2 Scientists, 2012. The Second Ulleung Basin Gas Hydrate Drilling Expedition (UBGS2). Expedition Report. Korea Institute of Geoscience and Mineral Resources, p. 666. Schultz, P.S., Ronen, S., Mattori, M., Corbett, C., 1994. Seismic-guided estimation of log properties, Part 1. The Leading Edge 13, 305e310, 315. Shelander, D., Dai, J., Bunge, G., 2010. Predicting saturation of gas hydrates using prestack seismic data, Gulf of Mexico. Marine Geophysical Researches 31, 39e57. Shelander, D., Dai, J., Bunge, D., Singh, S., Eissa, M., Fiser, K., 2012. Estimating saturation of gas hydrates using conventional 3D seismic data, Gulf of Mexico Joint Industry Project Leg II. Marine and Petroleum Geology 34, 96e110. Sibuet, J.C., Hsu, S.K., Le Pichon, X., Le Formal, J.P., Reed, D., Moore, G., Liu, C.S., 2002. East Asia plate tectonics since 15 Ma: constraints from the Taiwan Region. Tectonophysics 344, 103e134. Tamaki, K., Suyehiro, K., Allan, J., Ingle Jr., J.C., Pisciotto, K.A., 1992. Tectonic synthesis and implications of Japan Sea ODP drilling. In: Tamaki, K., Suyehiro, K., Allan, J., McWilliams, M. (Eds.), Proceedings of ODP Scientific Results Scientific Results. Part 2. Ocean Drilling Program, College Station, Texas, vol. 127/128, pp. 1333e1348. Williams, T.E., McDonald, W.J., Millheim, K., King, B., 2003. Methane Hydrate Production from Alaskan Permafrost. Technical Progress Report. Maurer Technology Inc., p. 221. DE-FC26e01NT41331.