Seismic diffractions for gas leakage detection in the shallowwaters of the Gulf of Mexico

Alexander Klokov1, Ramon H. Trevino1, Timothy A. Meckel1

1Bureau of Economic Geology, The University of Texas at Austin

(September 7, 2017)

Running head: Diffraction imaging with P-Cable data

ABSTRACT

Successful carbon capture and storage (CCS) requires secure CO2 confinement within a

geologic reservoir. Examination of the shallow subsurface for hydrocarbon leakage allows

conclusions about the sealing capability of the underlying confining systems (caprocks or

seals). Numerous seismic signatures have been reported to be hydrocarbon indicators. The

interpretation can be advanced by using of seismic diffractions, which could indicate subtle

hydrocarbon accumulations not detectable by conventional techniques. We investigate the

possibility of using seismic diffractions to detect shallow gas. We extract diffractions from

the ultra-high-resolution 3D P-Cable seismic dataset acquired along the Gulf of Mexico

inner continental shelf. Interpretation of this dataset revealed numerous seismic signatures

associated with hydrocarbon accumulations. In particular, a prominent gas chimney was

identified. We analyze scattering features of the detected hydrocarbon accumulations and

confirm the correlation between diffractions and hydrocarbon saturation. Based on these

observations, we interpret seismic diffractions to predict other hydrocarbon accumulations

— those that are not detectable by conventional techniques. In addition, we discuss other

subsurface features (salt dome, channel morphology, Chenier plain) that appear strong

1

sources of seismic scattering.

2

INTRODUCTION

Effective implementation of a carbon capture and storage (CCS) program requires a superior

reservoir. In addition to high capacity and high permeability, the reliable CO2 storage

should be securely sealed to ensure keeping the carbon dioxide in place over tens of thousands

of year. Therefore, detailed investigation of confining systems (caprocks or seals), their

integrity and sealing properties are an important component of prospecting for potential

CO2 storage sites.

Miocic et al. (2016) evaluated natural CO2 reservoirs, in which the carbon dioxide has

been trapped for million years, and discussed the main factors determining the storage

security, which included thickness of the confining system, reservoir depth, and gas density.

Fault and fractures were reported as the main conductors for migration of CO2 within the

subsurface. Preliminary assessment of storage security can be done by seismic data analysis.

In addition to identifing reservoirs and evaluating of stratal thicknesses, seismic can detect

faults in the confining system and overlying strata (Juhlin et al., 2007; Alcalde et al., 2013).

In combination with geomechanics, this information is critical for determining the optimal

configuration of a potential storage site (Vidal-Gilbert et al., 2010; Teatini et al., 2014;

Ward et al., 2016; White et al., 2016).

If associated with a depleted hydrocarbon reservoir, confining properties may be de-

termined by investigating the overlying strata for hydrocarbon accumulations. Minimal

indication of hydrocarbons suggests reasonbable sealing properties. Conversely, an increase

of deteceted gas concentrations above the reservoir indicates poor confining properties.

Løseth et al. (2009) presented an extended overview of seismic features associated with

hydrocarbon leakage. Hydrocarbon saturation causes amplitude anomalies (bright spots,

3

polarity reversals, dim spots), which have been exploited as a hydrocarbon indicators for

decades (Brown and Abriel, 2014). These features are mostly related to reservoir units.

However, they may be observed in rocks with low permeability as well (Løseth et al., 2009).

One prominent seismic signature associated with hydrocarbon leakage is a gas chimney,

which is a vertical zone with discontinous reflectors (Heggland, 1998; Singh et al., 2016).

The migrating gas causes irregular changes in the compressional velocity field that yields

scattering and degradation of reflected waves (Arntsen et al., 2007). Zhu et al. (2012)

investigated seismic imaging of a target in offshore China and concluded that wave scattering

caused by shallow gas is the primary phenomenon causing the reflected waves degradation.

Since local hydrocarbon accumulations, as any local heterogeneities in terms of acoustic

properties, scatter seismic energy, they can be identified by analysis of seismic diffrac-

tion waves. The linkage between hydrocarbon accumulations and seismic diffractions has

been documented in various case studies (Rauch-Davies et al., 2014; Klokov et al., 2014;

Ogiesoba and Klokov, 2015; Schoepp et al., 2015; Klokov et al., 2015). In contrast to conven-

tional methods, diffraction analysis allows operating with much weaker seismic signals and,

thereby, identification of subtle hydrocarbon accumulations or even just increases in hydro-

carbon concentration. Klokov et al. (2017) analyzed seismic diffractions from ultra-high-

resolution 3D (UHR3D) seismic data acquired on the inner shelf of the Gulf of Mexico (near

Bolivar Peninsula, Figure 1) using a P-CableTM acquisition system (Petersen et al., 2010;

Lippus, 2014) to evaluate fluid migration above potential reservoirs. Diffraction anomalies

have been reported and interpreted as evidence of hydrocarbon migration. Finally, poor

sealing capabilities of underlying confining zones has been concluded.

In this work, we further investigate the potential of seismic diffractions for use in shallow

4

gas detection. We utilize a P-Cable dataset acquired in the Texas State Waters near the San

Luis Pass (Figure 1). Interpreting the same dataset, Meckel and Mulcahy (2016) described

various seismic anomalies associated with hydrocarbon accumulations. We compare these

anomalies with seismic diffraction signatures to examine scattering features of hydrocarbon

saturated zones. Then, we interpret weaker diffraction signals to identify subtle hydrocarbon

accumulations not imagible with conventional seismic attribute analysis.

GEOLOGICAL SETTINGS

Addressing seismic attribute analysis of the same dataset used in the current study, Meckel

and Mulcahy (2016) mapped and interpreted two Quaternary age sequence boundaries and

related incised valley systems that were associated with two glacial lowstands (approxi-

mately 140 ka and 20 ka, respectively). They based their interpretation of the local geology

on the many preceding studies (Berryhill et al., 1987; Paine, 1991; Anderson et al., 1996;

Abdullah et al., 2004; Simms et al., 2007). Following from the interpretation of Abdullah

et al. (2004), a significant portion of the Pleistocene section of the study area is composed

of a delta lobe of the paleo Brazos River. The lobe is interpreted to have been deposited

during oxygen isotope stage 5e early in the Wisconsin interstadial and have aggradational to

progradational clinoform configurations. The 20 ka sequence boundary identified by Meckel

and Mulcahy (2016) is above lobe 5e; whereas, the 140 ka sequence boundary is below. The

incised valleys correlated with the two sequence boundaries include seismic facies typical

of fluvial channels (e.g., scours, point bars, lateral accretions). The interfluvial deposits

outside the incised valleys comprise seismic facies, which were interpreted as coarse-grained

channel scour deposits of a meandering channel and transgressive estuarine to marine, fine-

grained mud fill. The natural gas accumulations in the coarser grained deposits are the

5

focus of the current study.

DATA ACQUISITION AND PROCESSING

The P-Cable technology provides a specific acquisition configuration that allows imaging of

near subsurface with extremely high resolution (Petersen et al., 2010; Lippus, 2014). The

UHR3D dataset utilized for the current study was acquired in the Texas State Waters in

close proximity to San Luis Pass. The acquisition system utilized 12 streamers spaced at

12.5 m and armed with 8 channels each. Shots were spaced at 12.5 m and the source-receiver

offset was 110 m. Seismic impulse was generated by a single 90 cubic inch GI air gun, which

produced seismic data with a dominant frequency of 150 Hz. The P-Cable survey produced

high-resolution seismic imaging (bin size of 6.25 m) over the area of about 32 square km.

The acquisition parameters described in detail by Meckel and Mulcahy (2016). The data

were processed at the Bureau of Economic Geology, The University of Texas at Austin as

discussed in detail by Hess et al. (2014).

VELOCITY MODEL BUILDING

P-Cable surveys are featured by short offsets. This causes non-significant velocity depen-

dence of CMP gathers. And the traditional investigation of reflection arrivals along offset

(NMO analysis) does not produce a robust velocity estimation.

The velocity model building issue can be resolved by analysis of a diffraction image. Any

scatterer is imaged with the highest focusing if migrated with correct velocity. Using lower

or higher migration velocity, the scatterer smears along a hyperbola oriented down or up

respectively. Klokov et al. (2017) used the diffraction focusing/defocusing feature to build

a migration velocity model for the Bolivar P-Cable dataset collected approximately 80 km

6

northeast of the current area of study. Evaluation of numerous scatterers was integrated

to obtain a one-dimensional velocity profile. The intent of that study was to evaluate

hydrocarbon distribution instead of accurate locating of subsurface objects. Therefore,

the approximate velocity model was acceptable. Similar objectives and the short distance

between the two surveys make it reasonable to use the velocity model developed for the

Bolivar dataset for the current work.

DIFFRACTION ANALYSIS

To extract diffractions from the 3D stack volume and obtain a 3D diffraction image, we

followed the approach developed by Klokov and Fomel (2012). In addition to reflection

elimination, this method allows effective segregation of diffractions from background noise

that, in turn, favors interpretation of subtle diffraction signals. To facilitate interpretation,

the diffraction image was transformed into a diffraction energy attribute volume by energy

integration over a time gate of 4 ms. In contrast to many conventional seismic attributes,

diffraction energy is directly linked to acoustic properties of the subsurface. Thus, in addi-

tion to locating subsurface heterogeneities, a diffraction image can be used to characterize

the scatterers. In terms of hydrocarbon detection, higher diffractivity often appears as-

sociated with higher hydrocarbon saturation (Rauch-Davies et al., 2014; Schoepp et al.,

2015).

Next we consider some seismic signatures associated with hydrocarbon saturation and

investigate how they appear in the diffraction image.

7

Bright spot

When hydrocarbon replaces brine in a sandstone rock, it reduces the local acoustic impedance

because of lower velocity and density. If the acoustic impedance of the sandstone was ini-

tially lower than the acoustic impedance of the enclosing shales (which is typical for young

clastic sediments), such reduction leads to increased acoustic contrast between the reservoir

rock and sealing formations (Brown and Abriel, 2014). The higher contrast, in turn, is ex-

pressed in higher seismic amplitudes. These amplitude anomalies (bright spots) have been

routinely used for hydrocarbon detection. Since P-Cable surveys target the shallow sub-

surface, prospecting for bright spots is a reasonable strategy for hydrocarbon distribution

assessment.

Figure 2 shows a group of bright spots (solid outline) detectable at inline 154 and a time

of 92 ms. In the diffraction image, these bodies appear as strong anomalies as well. The

scattering features of bright spots can be easily explained when the heterogeneity (caused

by hydrocarbon saturation) is small and can be considered as a point scatterer. However, we

observe large bright spots: the largest one exceeds 140 m (which is 14 wavelengths) in cross

section. Therefore, we associate the diffractivity with irregular hydrocarbon distribution in

the formation. This likely causes local velocity and density anomalies that favor scattering

of seismic energy.

Diffraction imaging suggests that two detected bright spot areas are hydraulically con-

nected by a narrow link indicated by red arrows in the diffraction energy time slice. Fig-

ure 3 displays the cross section AA’, which reveals a vertical diffraction anomaly extended

up from the fluvial channel (blue arrow). This feature can be interpreted as a fault (or

fracture zone) caused by differential compaction associated with the channel (Allen and

8

Allen, 2005). Note that the supposed faulting/fracturing is not clearly detectable in the

conventional image time slice (Figure 2c). This can be explained by low diffraction energy

suppressed by stronger reflections and background noise.

Thus, bright spots, which are traditionally considered as a direct hydrocarbon indicator,

are detected as amplitude anomalies in the diffraction image. However, zones of high

diffractivity appear more extended in the diffraction energy time slice (Figure 2d). This

diffractivity can be caused by faulting and fracturing, which provides migration paths for

the hydrocarbons manifested as bright spots.

Gas chimney

Gas chimneys are another set of prominent seismic features used for hydrocarbon detection

(Heggland, 1998; Singh et al., 2016). They are characterized by scattering of seismic energy

and reflection wave degradation caused by hydrocarbon saturation. Meckel and Mulcahy

(2016) interpreted a gas chimney in the southwest part of the San Luis Pass P-Cable survey.

The chimney was clearly detectable in attribute sections and confirmed by gas analysis of

cores collected in the shallow sequence.

The gas chimney can be clearly seen in Figure 4 as a zone in which reflectors appear

strongly corrupted. The hydrocarbon migration is provided by a system of faults and

fractures, which are sources of diffraction waves. The scattering power of these faults is

boosted by hydrocarbon saturation because of higher acoustic impedance contrast between

fault zones and accommodating rocks. Therefore, the gas chimney is observed as a strong

amplitude anomaly in the diffraction image.

Using the similarity attribute, which is a response of coherency between neighboring

9

traces (de Rooij and Tingdahl, 2002), on a time slice from 130 ms (Figure 5), the gas

chimney can be delineated on three sides where the attribute intensity changes significantly.

On the fourth side, the gas chimney feature is demarcated by a fault plane (F1).

In addition to the prominent fault plane F1, the diffraction imaging reveals two strong

linear scattering features F2 and F3. Because of the azimuth consistency with F1, we also

interpret these features as fault planes. Note that they are not easily detectable on the

similarity attribute. This may be explained by high gas saturation around the fault zones;

thus, a significant amount of seismic energy is scattered that obscures imaging and analysis

of the reflection boundaries. Diffraction imaging, in turn, allows collecting the scattered

energy and utilizing it for subsurface imaging. Feature F4 probably results from the same

phenomenon.

Gas chimneys confined by faults and associated with gas migrating along the faults

are classified as Type I (Heggland, 2005). The faults providing migration paths are easily

detectable in the diffraction image as vertical high-amplitude features (some are indicated

by arrows in Figure 4).

Fault trap

The observed correlation between confidently interpreted gas accumulations and seismic

scattering adds validity to using seismic diffractions as hydrocarbon indicators. Figure 6

shows a high-amplitude diffraction anomaly (solid outline), which is associated with a dip-

ping structure and trapped against a fault plane. The fault, in contrast to those shown

in Figure 4, does not appear as a strong scatterer. This can be explained by minor fluid

saturation within the fault zone, which means a sealing fault with no fluid migration along

the fault plane in this location. So, the diffraction anomaly can be interpreted as a hy-

10

drocarbon (likely methane) accumulated in a structural trap. Low diffractivity above the

anomaly suggests low permeability sediments sealing the trap.

The interpreted gas accumulation does not stand out in the conventional image (Fig-

ure 6a). Note also that the conventional image looks seriously corrupted by acquisition

footprint (Figure 6c). Diffraction imaging allows noise suppression that facilitates interpre-

tation of the data.

OTHER SCATTERING FEATURES

In the previous section, we discussed the linkage between seismic diffractions and hydrocar-

bon saturation. In addition, seismic scattering can be caused by abrupt changes in lithology.

Below, we review some subsurface scattering features detected by diffraction imaging.

Salt dome

The San Luis Pass P-Cable survey partially illuminated the San Luis Pass Salt Dome

(Nettleton, 1957). The salt body is located in the southeast part of the survey; it is

easily detectable in the conventional seismic image and appears as a strong anomaly in

the diffraction image (Figures 7 and 8). First, this is caused by high acoustic contrast

between the salt and adjacent clastic sediments. Second, salt domes can have sharp edges

which scatter seismic energy. Note that some scattering points are detected inside the salt

body, which possibly indicates heterogeneous structure of the salt. In addition, diffraction

imaging illuminates some radial faults associated with the salt extension (Figure 7b). High

diffraction energy observed at these faults can be interpreted as indicators of fluid migrating

along the fault planes.

11

Channel morphology

Meckel and Mulcahy (2016) mapped an unconformity (referred as UC2) and interpreted

it as an incised valley associated with the OIS 6 lowstand, which was attributed to about

140 ka (Simms et al., 2007). Figure 9 shows the UC2 horizon slice. It reveals a channel

system with multiple ravines roughly orthogonal to the main channel.

In the diffraction image, the ravines are associated with strong diffraction anomalies.

The main channel, in turn, reveals low-to-moderate scattering power. The ravines can

accumulate less sorted coarser sediments that favor creation of local velocity and density

anomalies. These zones have higher scattering features, which is detectable by diffraction

imaging. The similar interpretation can be applied to point bar deposits identified within

the channel (Meckel and Mulcahy, 2016). Figure 10 reproduces an example in which dipping

reflections were interpreted as coarse-grain bar deposits. This zone demonstrates high

scattering features indicating high acoustic contrast to adjacent sediments.

Chenier plain

Figure 11 is interpreted as an example of a strand plain comprising multiple beach ridges

subparallel to the ancient shoreline (Otvos and Price, 1979; Augustinus, 1989). In the

diffraction energy slice, the strand plain comprises long and narrow high-amplitude features

interpreted as beach ridges. Because of wave-winnowing, beach ridges are often composed of

coarser material. This makes them lithologically heterogeneous that favors seismic energy

scattering. High porosity and fluid saturation can enhance the scattering power of the beach

ridges.

12

RESULTS AND DISCUSSION

A P-Cable survey acquired along the inner shelf of the Gulf of Mexico revealed numerous

seismic signatures associated with hydrocarbon accumulations. We compared these signa-

tures with seismic diffractions. Observed correlation between diffractivity and hydrocarbon

saturation supports utilizing seismic diffractions for shallow gas detection. The ability to

validate small accumulations or low concentrations of natural gas could, in turn, increase

reliability of CO2 storage assessment by identifying areas of active natural fluid migration.

Two conventional hydrocarbon indicators, gas chimney and bright spot, were associated

with high-amplitude anomalies in the diffraction image. Strong diffractions attributed to

the gas chimney is caused by significant faulting and fracturing. The observed bright spots

occupied large areas that significantly exceeded the seismic wavelength. Therefore, it is

not likely that they were acting as point diffractors. The scattering phenomenon could be

explained by irregular distribution of migration pathways, which are faulted and fractured

zones.

The correlation between hydrocarbon saturation and seismic scattering allows using

seismic diffractions for locating shallow gas not easily detectable by conventional seismic

attributes. We interpreted a high-amplitude diffraction anomaly, which was associated

with dipping structure and confined by a fault plane. Diffraction imaging also suggested a

fractured zone that could provide hydraulic connections between observed bright spots.

In addition to hydrocarbon saturated zones, we discussed other subsurface scattering

objects that were exposed by diffraction imaging. We observed high diffractivity from point

bar deposits, channel ravines, salt dome, and Chenier plain. For correct interpretation of a

diffraction image, these features should be taken into consideration.

13

ACKNOWLEDGMENTS

Research was supported by the U.S. Department of Energy, National Energy Technology

Laboratory, DE-FE0026083. The P-Cable data were processed by Thomas Hess. We thank

Vladimir Moskalev and Osareni Ogiesoba for helpful discussions. We thank Caroline Breton

for assistance with the graphics. Seismic attribute analysis was performed in the OpendTect

environment.

Disclaimer: “This report was prepared as an account of work sponsored by an agency

of the United States Government. Neither the United States Government nor any agency

thereof, nor any of their employees, makes any warranty, express or implied, or assumes any

legal liability or responsibility for the accuracy, completeness, or usefulness of any informa-

tion, apparatus, product, or process disclosed, or represents that its use would not infringe

privately owned rights. Reference herein to any specific commercial product, process, or ser-

vice by trade name, trademark, manufacturer, or otherwise does not necessarily constitute

or imply its endorsement, recommendation, or favoring by the United States Government or

any agency thereof. The views and opinions of authors expressed herein do not necessarily

state or reflect those of the United States Government or any agency thereof.”

14

REFERENCES

Abdullah, K., J. Anderson, J. Snow, and L. Holdford-Jack, 2004, The Late Quaternary

Brazos and Colorado Deltas, Offshore Texas, USA — Their evolution and the factors

that controlled their deposition: SEPM Special Publication, 79, 237–269.

Alcalde, J., D. Mart, A. Calahorrano, I. Marzan, P. Ayarza, R. Carbonell, C. Juhlin,

and A. Perez-Estaun, 2013, Active seismic characterization experiments of the Hontomın

research facility for geological storage of CO2, Spain: International Journal of Greenhouse

Gas Control, 19, 785–795.

Allen, P. A., and J. R. Allen, 2005, Basin analysis: Principles and applications, 2nd edition:

Wiley-Blackwell.

Anderson, J. B., K. Abdullah, S. Sarzalejo, F. Siringan, and M. A. Thomas, 1996, Late

Quaternary sedimentation and high-resolution sequence stratigraphy of the east Texas

shelf: in M. de Batist, and P. Jacobs, eds., Geology of siliciclastic seas: Geological Society

of London, Special Publication, 117, 95–124.

Arntsen, B., L. Wensaas, H. Løseth, and C. Hermanrud, 2007, Seismic modeling of gas

chimneys: Geophysics, 72, SM251–SM259.

Augustinus, P., 1989, Cheniers and chenier plains: A general introduction: Marine Geology,

90, 219–229.

Berryhill, H. L. J., J. R. Suter, and N. S. Harden, 1987, Later Quaternary facies and struc-

ture, northern Gulf of Mexico: Interpretations from seismic data: American Association

of Petroleum Geologists, 23.

Brown, A. R., and W. L. Abriel, 2014, Detection of hydrocarbons using non-bright-spot

seismic techniques: Interpretation, 2, SP1–SP4.

de Rooij, M., and K. Tingdahl, 2002, Meta-attributes — the key to multivolume, multiat-

15

tribute interpretation: The Leading Edge, 21, 1050–1053.

Heggland, R., 1998, Gas seepage as an indicator of deeper prospective reservoirs. A study

based on exploration 3D seismic data: Marine and Petroleum Geology, 15, 1–9.

——–, 2005, Using gas chimneys in seal integrity analysis: A discussion based on case

histories: in P. Boult and J. Kaldi, eds., Evaluating fault and cap rock seals: AAPG

Hedberg Series, 2, 237–245.

Hess, T., T. Meckel, N. Bangs, and R. Tatham, 2014, Case history of acquisition and

processing of a high resolution shallow water 3D multi-cable seismic survey in the Gulf

of Mexico transition zone: SEG Technical Program Expanded Abstracts, 208–212.

Juhlin, C., R. Giese, K. Zinck-Jørgensen, C. Cosma, H. Kazemeini, N. Juhojuntti, S. Luth,

B. Norden, and A. Forster, 2007, 3D baseline seismics at Ketzin, Germany: The CO2SINK

project: Geophysics, 72, B121–B132.

Klokov, A., and S. Fomel, 2012, Separation and imaging of seismic diffractions using mi-

grated dip-angle gathers: Geophysics, 77, S131–S143.

Klokov, A., D. Irkabaev, O. Ogiesoba, K. Skachek, and N. Munasypov, 2014, Diffraction

analysis for the Sortym Formation using vertical seismic profiling data: Journal of Seismic

Exploration, 23, 463–480.

Klokov, A., D. Irkabaev, O. C. Ogiesoba, and N. Munasypov, 2015, Correlation between

seismic diffractions extracted from vertical seismic profiling data and borehole logging in

a carbonate environment: Interpretation, 3, T121–T129.

Klokov, A., R. H. Trevino, and T. A. Meckel, 2017, Diffraction imaging for seal evaluation

using ultra high resolution 3D seismic data: Marine and Petroleum Geology, 82, 85–96.

Lippus, C., 2014, High-resolution revolution: OilField Technology, 7, 57–62.

Løseth, H., M. Gading, and L. Wensaas, 2009, Hydrocarbon leakage interpreted on seismic

16

data: Marine and Petroleum Geology, 26, 1304–1319.

Meckel, T. A., and F. J. Mulcahy, 2016, Use of novel high-resolution 3D marine seismic

technology to evaluate Quaternary fluvial valley development and geologic controls on

shallow gas distribution, inner shelf, Gulf of Mexico: Interpretation, 4, SC35–SC49.

Miocic, J. M., S. M. Gilfillan, J. J. Roberts, K. Edlmann, C. I. McDermott, and R. S.

Haszeldine, 2016, Controls on CO2 storage security in natural reservoirs and implications

for CO2 storage site selection: International Journal of Greenhouse Gas Control, 51,

118–125.

Nettleton, L. L., 1957, Submarine gravity detailing, San Luis Pass Dome, Brazoria County,

Texas: Geophysics, 22, 348–358.

Ogiesoba, O. C., and A. Klokov, 2015, Diffraction imaging of lithology and fluid saturation

in fault zones within the Austin Chalk and Eagle Ford Shale, Maverick Basin, South

Texas: SEG Technical Program Expanded Abstracts, 5142–5146.

Otvos, E., and W. A. Price, 1979, Problems of chenier genesis and terminology – an

overview: Marine Geology, 31, 251–263.

Paine, J. G., 1991, Late Quaternary depositional units, sea level, and vertical movement

along the central Texas coast: Ph.D. thesis, The University of Texas at Austin.

Petersen, C. J., S. Bunz, S. Hustoft, J. Mienert, and D. Klaeschen, 2010, High-resolution

P-Cable 3D seismic imaging of gas chimney structures in gas hydrated sediments of an

Arctic sediment drift: Marine and Petroleum Geology, 27, 1981–1994.

Rauch-Davies, M., K. Deev, D. Pelman, and M. Kachkachev-Shuifer, 2014, Diffraction imag-

ing applied to pre-existing 3D seismic data to map fracture corridors in an unconventional

play: First Break, 32, 87–90.

Schoepp, A., S. Labonte, and E. Landa, 2015, Multifocusing 3D diffraction imaging for

17

detection of fractured zones in mudstone reservoirs: Case history: Interpretation, 3,

SF31–SF42.

Simms, A. R., J. B. Anderson, K. T. Milliken, Z. P. Taha, and J. S. Wellner, 2007, Geo-

morphology and age of the oxygen isotope stage 2 (last lowstand) sequence boundary on

the northwestern Gulf of Mexico continental shelf: Geological Society of London, 277,

29–46.

Singh, D., P. C. Kumar, and K. Sain, 2016, Interpretation of gas chimney from seismic

data using artificial neuralnetwork: A study from Maari 3D prospect in the Taranaki

basin,New Zealand: Journal of Natural Gas Science and Engineering, 36, 339–357.

Teatini, P., N. Castelletto, and G. Gambolati, 2014, 3D geomechanical modeling for CO2

geological storage in faulted formations. A case study in an offshore northern Adriatic

reservoir, Italy: International Journal of Greenhouse Gas Control, 22, 63–76.

Vidal-Gilbert, S., E. Tenthorey, D. Dewhurst, J. Ennis-King, P. V. Ruth, and R. Hillis,

2010, Geomechanical analysis of the Naylor Field, Otway Basin, Australia: Implications

for CO2 injection and storage: International Journal of Greenhouse Gas Control, 4, 827–

839.

Ward, N. I., T. M. Alves, and T. G. Blenkinsop, 2016, Reservoir leakage along concentric

faults in the Southern North Sea: Implications for the deployment of CCS and EOR

techniques: Tectonophysics, 690, 97–116.

White, D., C. Hawkes, and B. Rostron, 2016, Geological characterization of the Aquistore

CO2 storage site from 3D seismic data: International Journal of Greenhouse Gas Control,

54, 330–344.

Zhu, X., K. Wallace, Q. Zhu, and R. Hofer, 2012, Scattering effect on shallow gas-obscured

zone imaging in Bohai PL19-3 area: Geophysics, 77, B43–B53.

18

LIST OF FIGURES

1 Map of the Texas Coastal Bend and Texas State Waters showing P-Cable survey

location. Clemente-Tomas Fault outline is from Seni et al. (1997); fault located where it

offsets the top of the Miocene geologic section.

2 Bright spots (solid outline) detected in the conventional time-migrated image (a,

c). In the diffraction energy attribute (b, d), these signatures appear as high-amplitude

anomalies. Red arrows indicate possible linkage between two bright spots.

3 Conventional time-migrated image section (a) and diffraction energy section (b)

for profile AA’ indicating a channel (blue arrow) and associated fracture zone (red arrow),

which provides hydraulic connection between two bright spots. The channel is clearly seen

in the conventional image time slice (c).

4 Inline sections for (a) conventional time-migrated image, (b) similarity attribute,

and (c) instantaneous amplitudes detecting the gas chimney. In the diffraction energy

section (d), the gas chimney appear as high-amplitude anomaly. Arrows indicate faults

providing paths for the hydrocarbon migration.

5 Time slice from 130 ms for (a) similarity attribute and (b) diffraction energy de-

tecting the gas chimney (solid outline). Arrows indicate interpreted fault planes.

6 Inline section and time slice for (a, c) conventional time-migrated image and (b,

d) diffraction energy. The diffraction anomaly (solid outline) is interpreted as hydrocarbon

accumulation associated with a structural trap. Bold solid lines indicate a fault plane.

7 Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-

tion energy. Salt dome is a strong source of seismic diffractions. Red arrows indicate radial

faults associated with the salt dome.

8 Inline section for (a) conventional time-migrated image and (b) diffraction energy.

19

Salt dome is a strong source of seismic diffractions.

9 Horizon view for the UC2 unconformity: (a) two-way traveltime and (b) diffrac-

tion energy. Strong diffractions are concentrated in the ravines (red arrows) suggesting less

sorted coarser sediments accumulations.

10 Inline section for (a) conventional time-migrated image and (b) diffraction energy.

Point-bar deposits (white dashed outline) corresponds to the high-amplitude diffraction

anomaly.

11 Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-

tion energy. Chenier plain features (red arrows) appear as sources of diffracted waves.

20

Figure 1: Map of the Texas Coastal Bend and Texas State Waters showing P-Cable surveylocation. Clemente-Tomas Fault outline is from Seni et al. (1997); fault located where itoffsets the top of the Miocene geologic section.Klokov et al. –

21

����������

�������� �

��

� �

� �

� �� ��

� ��

���

�����

��

Figure 2: Bright spots (solid outline) detected in the conventional time-migrated image (a,c). In the diffraction energy attribute (b, d), these signatures appear as high-amplitudeanomalies. Red arrows indicate possible linkage between two bright spots.Klokov et al. –

22

����������

� ����� ����

� ����� ����

�

��

� ��

�

� ��

� ��

� ��

�����

��

Figure 3: Conventional time-migrated image section (a) and diffraction energy section (b)for profile AA’ indicating a channel (blue arrow) and associated fracture zone (red arrow),which provides hydraulic connection between two bright spots. The channel is clearly seenin the conventional image time slice (c).Klokov et al. –

23

�� ����� ���

�� ����� ���

�

�

�

�

��

� ��

�����

��

Figure 4: Inline sections for (a) conventional time-migrated image, (b) similarity attribute,and (c) instantaneous amplitudes detecting the gas chimney. In the diffraction energysection (d), the gas chimney appear as high-amplitude anomaly. Arrows indicate faultsproviding paths for the hydrocarbon migration.Klokov et al. –

24

���� ����

��������

����

��

���� ����

������������

����

��

�

�

� ��

Figure 5: Time slice from 130 ms for (a) similarity attribute and (b) diffraction energydetecting the gas chimney (solid outline). Arrows indicate interpreted fault planes.Klokov et al. –

25

��������

����������

� �

� �

� ��� ��

� ��� ��

� ��

�����

��

Figure 6: Inline section and time slice for (a, c) conventional time-migrated image and (b,d) diffraction energy. The diffraction anomaly (solid outline) is interpreted as hydrocarbonaccumulation associated with a structural trap. Bold solid lines indicate a fault plane.Klokov et al. –

26

�

�

� ��� ��

Figure 7: Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-tion energy. Salt dome is a strong source of seismic diffractions. Red arrows indicate radialfaults associated with the salt dome.Klokov et al. – 27

�

�

�� ��

Figure 8: Inline section for (a) conventional time-migrated image and (b) diffraction energy.Salt dome is a strong source of seismic diffractions.Klokov et al. –

28

�

�

��

Figure 9: Horizon view for the UC2 unconformity: (a) two-way traveltime and (b) diffractionenergy. Strong diffractions are concentrated in the ravines (red arrows) suggesting lesssorted coarser sediments accumulations.Klokov et al. –

29

������ ������

� �

� ��� ��

� ��

�����

��

Figure 10: Inline section for (a) conventional time-migrated image and (b) diffraction energy.Point-bar deposits (white dashed outline) corresponds to the high-amplitude diffractionanomaly.Klokov et al. –

30

�

�

� ��

Figure 11: Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-tion energy. Chenier plain features (red arrows) appear as sources of diffracted waves.Klokov et al. – 31