Predicting Gas Hydrates Using Prestack Seismic Data in

Deepwater Gulf of Mexico (JIP Projects)

Dianna Shelander1, Jianchun Dai2, George Bunge1,

Dan McConnell3, Niranjan Banik2

1 Schlumberger / DCS

2 Schlumberger/WesternGeco

3 AOA Geophysics

AAPG E-Symposium

February 11, 2010

2

Much appreciation goes to the JIP for permission to present this

work and to WesternGeco for their donation of the seismic data.

Many thanks to William Shedd (MMS) for his contributions.

Acknowledgement:

3

Introduction—why gas hydrates?JIP Gulf of Mexico gas hydrates project

How do we recognize it?Seismic characterization

How do we quantify it using seismic data?Interpretation and stratigraphic analysis

Data processing and conditioning

Seismic inversion

Rock physics analysis

Modeling

Summary

Outline

4

Potential Energy Source

100,000–3,000,000 tcf(vs. ~13,000 tcf from

conventional natural gas)

Greenhouse effect

CH4 has 22 times the

warming effect as CO2

Shallow hazard

Why Gas Hydrates?

1 ft3 164 ft3 0.8 ft3

Gas hydrate Gas Water

(Kvenvolden, 1988)

+

5

(Boswell and Collett, 2006)

Gas hydrate resource pyramid Nonhydrate gas resources

Estimated Gas Hydrates Resources

6

Shallow Hazard – (GOM, AC818)

Gas

hydrate

mound

with craterChannel

Ridges

Dip Attribute Map of Seafloor Hydrates Seep through Sediments

- +

CraterChannel

Gas

vent

1km

7

Known and Inferred Occurrences of Gas Hydrates

Edited from Kvenvolden (1998)

Gulf of Mexico (JIP)

Gas Hydrate Programs Worldwide—India, USA, Japan, China, South Korea, etc.

AC-21

WR-313

GC-955

X

X

KC-195

AT-14

0 300

kilometersShedd, et al., 20098

X JIP Leg I drill site (2005)

JIP Leg II drill site (2009)

Seismic indicators of Hydrates in Gulf of Mexico –

MMS has identified 100+ thus far

WR313

GC955

AC857AC818

9

Bottom Simulating Reflector (BSR)—Example Seismic (AC857)

Gas hydrates:

Increase VP

Increase VS

- +

10

Properties of hydrates

water hydrate

Compressional velocity, Vp (m/s) 1480 3800

Shear velocity, Vs (m/s) 0 1880

Density (gm/cc) 1.00 0.92

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10km

(Map courtesy of W. Shedd, MMS)

Terrebonne Basin Area (Purple Line)—Seafloor Relief Map

WR313

12

WR313 Seismic Example—Well Tie (Strike)

Sand-proneChannel

Sand-silt–prone

Clay-prone

Silt-prone

Silt-clay–prone

Sand stringers

NE SW

100 m/100 ms GR W

13

NW SEWR313 Seismic Example – (dip section)

- +

100m/100ms

NW SE

14

WR313 - Blue Horizon and Amplitude

Amplitude Structure (time)

- +High Low

N

1km1km

15

10 km

Green Canyon Seafloor Relief Map—Sediment Flow

GC955

16

Stratigraphic Evaluation (GC955)

GR

Well GC955-001SW NE

decreasing sand content

300m / 100ms

17

GC955 - C Horizon structure and gas source

1km

Min Amp. (100ms window, below BGHS)C Horz. Structure (time)

N

18

Sgh GC955 – max value, interval C Horizon - BGHS

Sgh (%)

0 100

H

Q

I

N

19

Sgh - WR313

Orange Horizon above BGHSBlue Horizon above BGHS

Sgh (%)

0 40

N

HG HG

1km

20

Estimating

Saturation of Gas Hydrates (Sgh)

with Prestack Seismic Data

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• Stratigraphic Analysis and Interpretation

• Seismic Data Processing and Conditioning

• Pre-Stack Waveform Inversion

• Simultaneous Inversion of pre-stack seismic data

• Rock Physics Modeling

• Saturation estimation through a Bayesian type approach

(integrating rock modeling

and seismic inversion)

How do we quantify GH using seismic data?

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• gas hydrates in porous sands - decrease in seismic amplitude with offset

• opposite to free gas in porous sands

Vp1

Pre-stack gather example - AVO inversion input

Vs1 Density1Vp2 Vs2 Density2

23

• optimize the signal-to-noise

• provide the best quantitative measurement of the true AVO signature

Conditioning pre-stack data inversion accuracy

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Smooth black curves

initial input models

Pre-Stack Waveform Inversion (PSWI) – GC955

Blue curves

derived pseudo logs

(PSWI)

Red curves

available logs

Zone of interest

Vp DensityVs

• generate control logs in the zone of interest

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Vp Rho PR Synthetic Real

PSWI pseudo logs: Vp, Poisson’s ratio, and density

width of the yellow band corresponds to uncertainties

PSWI Quality Control• best match and uncertainties (yellow) – GC955

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Wavelet analysis – on multiple angles GC955

wavelets are stable overall

small differences between angle offsets

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P Impedance S Impedance

Red curves:

PSWI pseudo logs

for comparison only

Simultaneous Inversion Quality Control – GC955

Blue curves:

inversion results

at the well location

= P velocity x Density = S velocity x Density

Smooth green curves:

input model

• generate P-impedance and S-impedance

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Simultaneous Inversion - Impedance volumes –GC955

P-impedance

S-impedance

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(Dai et al., 2004)

Model Responses

Model 3—Supporting matrix/grain model--hydrates grow in the interior of the porous frame and support the overburden together with the grains.

Data shown - Mallik 2L-38 well, Alaska.

The M3 model matches GOM and other locations.

Gas Hydrate Rock Models

Rock Models and Responses

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Rock Model - Sgh Trend Curves

P Impedance S Impedance

0% Sgh curve is based on:

• stratigraphic analysis and regional knowledge

• compaction trend

• tied to available logs below the zone of interest

Sgh

0% 100%

31

Sgh volumes – sand/shale model -GC955

Sgh (P-impedance)

Sgh (S-impedance)

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Velocity analyses on

spatially consistent horizons

High resolution velocity analysis – WR313

High frequency interval velocity dataset

low velocities=blues, high velocities=pinks

water bottom

BGHS

• independent of amplitude analyses (e.g. Sgh estimation)

33

Sgh - Random Line GC955 - (using shale-sand model)

W N

300m

100ms

Q well H well I well

34

Sgh - Random Line WR313

W E

G well H well

100m

100ms

BGHS

Sgh – WR313 well G (using shale-sand model)

NE SW

Sgh35

Sgh – WR313 well H (using shale-sand model)

Sgh

NE SW

GRW DT36

Sgh - Random Line WR313

W E

GRW DTG well H well37

Fracture analysis - Attribute vs. Ant track

Ant trackVariance

time slice

38

Fault / Fracture analysis - Ant track

Gulf of Mexico example39

40

Summary

Gas hydrates are potentially:- significant resource for natural gas to the world

- drilling/production hazard

Occurrence of gas hydrates

- polar regions of the earth

- deep marine basins

- in GOM, generally where water depths > 500m

Seismic data can identify and estimate concentrations of gas hydrates

- examples shown in WR313, GC955

- using pre-stack seismic data

- high concentrations of hydrates were successfully predicted

before 2009 JIP wells were drilled

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Summary

Methodology: an integrated ―five step‖ approach- Stratigraphic analysis and interpretation

provide geologic context

improve probability of finding better reservoirs

- Conditioning seismic gathers to ensure high quality AVO input data

- PreStack Waveform Inversion - generate pseudo logs in the stability zone

using Full Waveform Equation

- 3D simultaneous prestack inversion – generate Ip and Is volumes

including Multi-offset Wavelet Analysis

- Sgh quantification using rock physics models

using Bayesian statistical inversion

improves predictability

provides a measure of uncertainty

42

Looking Forward

Sgh quantification - calibration using new 2009 JIP well data

will improve accuracy in the stability zone

will improve identifying low to moderate saturations

GC955 high Sgh values occur below the estimated BGHS horizon

- understand these events

- more hydrates or something else?

(high resolution velocity analysis may help)

WR313 fracture filled hydrate zones (opportunity)

- a good mathematical model is needed

- good imaging is needed

(Ants technology may help)

43

Boswell, R., and Collett, T., 2006. The Gas Hydrate Resource Pyramid. Fire in the Ice,

Methane Hydrate R&D Program Newsletter

Dai, J., et al., 2004. Detection and estimation of gas hydrates using rock physics and

seismic inversion. The Leading Edge

Kvenvolden, K., 1988. Methane hydrates and global climate. Global Biochemical

Cycles

Kvenvolden, K. A., 1998. A primer on the geological occurrence of gas hydrate.

Geological Society, London

Shedd, W., et al., 2009. Variety of Seismic Expression of the Base of Gas Hydrate

Stability in the Gulf of Mexico, USA, AAPG Annual Convention and Exhibition, Denver,

Colorado

-Map of sediment pathways in Terrebonne (courtesy of Shedd, W., 2009)

References:

Predicting Gas Hydrates Using Prestack Seismic Data in

Deepwater Gulf of Mexico (JIP Projects)

Dianna Shelander1, Jianchun Dai2, George Bunge1,

Dan McConnell3, Niranjan Banik2

1 Schlumberger / DCS

2 Schlumberger/WesternGeco

3 AOA Geophysics