predicting gas hydrates - thepttc.org · predicting gas hydrates using prestack seismic data in...
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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
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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:
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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
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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)
+
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(Boswell and Collett, 2006)
Gas hydrate resource pyramid Nonhydrate gas resources
Estimated Gas Hydrates Resources
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Shallow Hazard – (GOM, AC818)
Gas
hydrate
mound
with craterChannel
Ridges
Dip Attribute Map of Seafloor Hydrates Seep through Sediments
- +
CraterChannel
Gas
vent
1km
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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.
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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
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Bottom Simulating Reflector (BSR)—Example Seismic (AC857)
Gas hydrates:
Increase VP
Increase VS
- +
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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
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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
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NW SEWR313 Seismic Example – (dip section)
- +
100m/100ms
NW SE
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WR313 - Blue Horizon and Amplitude
Amplitude Structure (time)
- +High Low
N
1km1km
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10 km
Green Canyon Seafloor Relief Map—Sediment Flow
GC955
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Stratigraphic Evaluation (GC955)
GR
Well GC955-001SW NE
decreasing sand content
300m / 100ms
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GC955 - C Horizon structure and gas source
1km
Min Amp. (100ms window, below BGHS)C Horz. Structure (time)
N
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Sgh GC955 – max value, interval C Horizon - BGHS
Sgh (%)
0 100
H
Q
I
N
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Sgh - WR313
Orange Horizon above BGHSBlue Horizon above BGHS
Sgh (%)
0 40
N
HG HG
1km
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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
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• 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%
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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)
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Sgh - Random Line GC955 - (using shale-sand model)
W N
300m
100ms
Q well H well I well
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Sgh - Random Line WR313
W E
G well H well
100m
100ms
BGHS
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Sgh – WR313 well G (using shale-sand model)
NE SW
Sgh35
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Sgh – WR313 well H (using shale-sand model)
Sgh
NE SW
GRW DT36
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Sgh - Random Line WR313
W E
GRW DTG well H well37
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Fracture analysis - Attribute vs. Ant track
Ant trackVariance
time slice
38
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Fault / Fracture analysis - Ant track
Gulf of Mexico example39
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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
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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)
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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:
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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