Predicting Gas Hydrates Using Prestack Seismic Data in Deepwater Gulf of Mexico (JIP Projects) Dianna Shelander 1, Jianchun Dai 2, George Bunge 1, Dan McConnell 3, Niranjan Banik 2 1 Schlumberger / DCS 2 Schlumberger/WesternGeco 3 AOA Geophysics AAPG E-Symposium February 11, 2010
Acknowledgement: 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. 2
Outline 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 3
Why Gas Hydrates? Potential Energy Source 100,000 3,000,000 tcf (vs. ~13,000 tcf from conventional natural gas) Greenhouse effect CH 4 has 22 times the warming effect as CO 2 Shallow hazard + 1 ft 3 164 ft 3 0.8 ft 3 Gas hydrate Gas Water (Kvenvolden, 1988) 4
Estimated Gas Hydrates Resources Gas hydrate resource pyramid Nonhydrate gas resources (Boswell and Collett, 2006) 5
Shallow Hazard (GOM, AC818) Dip Attribute Map of Seafloor Ridges Hydrates Seep through Sediments Channel Crater Channel Gas hydrate mound with crater Gas vent 1km - + 6
Known and Inferred Occurrences of Gas Hydrates Gas Hydrate Programs Worldwide India, USA, Japan, China, South Korea, etc. Gulf of Mexico (JIP) 7 Edited from Kvenvolden (1998)
Seismic indicators of Hydrates in Gulf of Mexico MMS has identified 100+ thus far X JIP Leg I drill site (2005) JIP Leg II drill site (2009) X AT-14 AC-21 AC818 AC857 KC-195 X WR313 WR-313 GC-955 GC955 8 kilometers 0 300 Shedd, et al., 2009
Bottom Simulating Reflector (BSR) Example Seismic (AC857) Gas hydrates: Increase V P Increase V S - + 9
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 10
Terrebonne Basin Area (Purple Line) Seafloor Relief Map WR313 10km 11 (Map courtesy of W. Shedd, MMS)
WR313 Seismic Example Well Tie (Strike) NE SW Sand-silt prone Clay-prone Silt-prone Sand-prone Channel 12 GR W Silt-clay prone Sand stringers 100 m/100 ms
- + WR313 Seismic Example (dip section) NW SE NW SE 13 100m/100ms
WR313 - Blue Horizon and Amplitude Structure (time) Amplitude N 1km High Low - + 14
Green Canyon Seafloor Relief Map Sediment Flow GC955 10 km 15
Stratigraphic Evaluation (GC955) SW Well GC955-001 NE decreasing sand content 16 GR 300m / 100ms
GC955 - C Horizon structure and gas source C Horz. Structure (time) Min Amp. (100ms window, below BGHS) N 17 1km
Sgh GC955 max value, interval C Horizon - BGHS N I Q H 0 100 Sgh (%) 18
Sgh - WR313 Blue Horizon above BGHS Orange Horizon above BGHS N G H G H 1km 19 0 40 Sgh (%)
Estimating Saturation of Gas Hydrates (Sgh) with Prestack Seismic Data 20
How do we quantify GH using seismic data? 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) 21
Pre-stack gather example - AVO inversion input gas hydrates in porous sands - decrease in seismic amplitude with offset opposite to free gas in porous sands Vp 1 Vs 1 Density 1 Vp 2 Vs 2 Density 2 22
Conditioning pre-stack data inversion accuracy optimize the signal-to-noise provide the best quantitative measurement of the true AVO signature 23
Pre-Stack Waveform Inversion (PSWI) GC955 generate control logs in the zone of interest Vp Vs Density Zone of interest Blue curves derived pseudo logs (PSWI) Smooth black curves initial input models Red curves available logs 24
PSWI Quality Control best match and uncertainties (yellow) GC955 Vp PR Rho Real Synthetic 25 PSWI pseudo logs: Vp, Poisson s ratio, and density width of the yellow band corresponds to uncertainties
Wavelet analysis on multiple angles GC955 wavelets are stable overall small differences between angle offsets 26
Simultaneous Inversion Quality Control GC955 generate P-impedance and S-impedance Red curves: PSWI pseudo logs for comparison only Blue curves: inversion results at the well location Smooth green curves: input model 27 P Impedance S Impedance = P velocity x Density = S velocity x Density
Simultaneous Inversion - Impedance volumes GC955 P-impedance S-impedance 28
Rock Models and Responses Gas Hydrate Rock Models Model Responses (Dai et al., 2004) 29 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.
Rock Model - Sgh Trend Curves Sgh 0% 100% 30 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 volumes sand/shale model -GC955 Sgh (P-impedance) Sgh (S-impedance) 31
High resolution velocity analysis WR313 independent of amplitude analyses (e.g. Sgh estimation) Velocity analyses on spatially consistent horizons High frequency interval velocity dataset low velocities=blues, high velocities=pinks water bottom BGHS 32
Sgh - Random Line GC955 - (using shale-sand model) W N 100ms 33 Q well H well I well 300m
Sgh - Random Line WR313 W E BGHS 100ms 34 G well H well 100m
Sgh WR313 well G (using shale-sand model) NE SW 35 Sgh
Sgh WR313 well H (using shale-sand model) NE SW 36 GRW DT Sgh
Sgh - Random Line WR313 W E 37 G well GRW DT H well
Fracture analysis - Attribute vs. Ant track time slice Variance Ant track 38
Fault / Fracture analysis - Ant track 39 Gulf of Mexico example
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 40
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 41
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) 42
References: 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) 43
Predicting Gas Hydrates Using Prestack Seismic Data in Deepwater Gulf of Mexico (JIP Projects) Dianna Shelander 1, Jianchun Dai 2, George Bunge 1, Dan McConnell 3, Niranjan Banik 2 1 Schlumberger / DCS 2 Schlumberger/WesternGeco 3 AOA Geophysics