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Pitfalls in Rock Properties and Lithology Prediction in Dolomite
John Logel Anadarko Canada Corp
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Objective To demonstrate that subtle but powerful seismic phenomenon can substantially harm your seismic data and your interpretation TLC processing and “new” algorithms may not be enough to help Quality control, detailed investigation of data, and proper prospecting and risk are critical.
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Dolomite as the reservoir The surprise prospect and well
Outline Dolomite as the reservoir The surprise prospect and well The Mistie The VSP results The enemy (High order interbed multiples, Q) Possible fixes and conclusions
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Hydrothermal Dolomite Process
Davies, et al.
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AI Dolo with 10 % por = LS 6% por = Shale
and economic properties Rock Properties Dolomite 2.87 g/cc 7000 P m/s 3960 S m/s 20090 AI 11365 SI 1.77 Vp/Vs 95 Gpa Bulk 45 Gpa Shear Limestone 2.71 g/cc 6450 P m/s 3440 S m/s 17479 AI 9322 SI 1.88 Vp/Vs 77 Gpa Bulk 32 Gpa Shear Shale 2.58 g/cc 5800 P m/s 2000 S m/s 15000 AI 5200 SI 2.9 Vp/Vs 21 Gpa Bulk 7 Gpa Shear 50 MM.day IP 50B – 3 TCF UR high spacing 10 MM/day IP 10 B – 50 B UR low drainage 0 MM /day IP 0 BCF No more drilling AI Dolo with 10 % por = LS 6% por = Shale Vp/Vs changes less
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Tuning curve for “Typical” Dolomite
reservoir amp 4 to 20 ms zone of anomolous amplitude For 45 Hz Dom freq isochron
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Dolomite Zero Offset Modeling
Dolomite Porosity Development Dolomite Tight Porous
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Processing Highlights
Boutique TLC Processing PSTM 2 Passes of velocities 2 passes and 2 different Algorithms of De-multiple (Radon and Inquisitor hyperbolic) Controlled Amp and Phase QC Controlled Scaling
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3 Window Amp extraction NO CAPP
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3 Window Amp extraction CAPP (Deep Win Scaler)
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3 Window Amp extraction Full CAPP
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LMR Interpretation Methodology
8 LambdaRho - MuRho Interpretation Template Crossplot Guide 250 MuRho Gpa .gm/cc LambdaRho GPa 125 POROUS DOLOMITE Gas Calcareous Shales Clastic SHALE SAND Wet 1:1 2:1 TIGHT LIMESTONE 100 50 150 200 Goodway etal.
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Co-rendered Amplitude and Linament detection
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Prospect Waveform classification map Prospect And leads
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Pre-drill Analysis Analogue Prospect lambda Mu Ratio diff
Prospect Analogue Well lambda Analogue Mu Ratio diff Prospect
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Drill the Well!!!!
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Post-Drill !! Prospect Analogue GR dt rhob GR dt rhob
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Raw Synthetic tie CC= .29 Zone of Interest
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What Now ??
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Petrophysicist
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Post edit Synthetic tie
CC=.4 Zone of Interest
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Processing Geophysicist
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Other Problems, Pitfalls and Corrections
Multiples Q (attenuation) VSP
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VSP showing potential sources of
Interbed multiples High velocity Low Velocity/Laminated Basin Fill Shales Outside Corridor Stack Inside Corridor Stack High Velocity Zone of Interest
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Zero (corridor) VSP stack Inside (multiple) stack seismic data
VSP – Data comparison Zero (corridor) VSP stack Inside (multiple) stack seismic data Zone of Interest
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VSP (downgoing colour – upgoing WVA)
with Multiples Interbed multiples First arrivals Interbed multiples
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Logs Elastic Modeling Comparison Elastic Model 90 degrees Elastic
Raw seismic Logs synthetics
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Q (attenuation) “Q” is the attenuation of frequency and the changes in phase because of it. Shales or other “soft” material are most Q prone Laminated material are also very Q prone If it can be determined it can be corrected (difficult)
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Wavelet extraction and response
Extracted wavelet including shallow Extracted wavelet deep Theoretical response
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Processed VSP and Q computation
Drift Correction Method Spectral Ratio Method
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Q Application correct qcomp orig
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Q application and Amplitude Envelope
Corrected Qcomp Orig
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VSP zos corrected model mult original
Final tie with synthetics validation VSP zos corrected model mult original
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Questions about Multiples????
Shouldn’t they be adequately attenuated? Aren’t they too low of amplitude to worry about? Aren’t they “constant”? Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response? Doesn’t the stack take care of it?
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Questions about Multiples????
Shouldn’t they be adequately attenuated? Aren’t they too low of amplitude to worry about? Aren’t they “constant”? Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response? Doesn’t the stack take care of it?
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2D expanding models Primaries only Primaries and Multiples
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2D expanding models Primaries only Primaries and Multiples
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Questions about Multiples????
Shouldn’t they be adequetely attenuated? Aren’t they too low of amplitude to worry about? Aren’t they “constant”? Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response? Doesn’t the stack take care of it?
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AVO response for Dolomite Reservoir
Reservoir AVO response CLASS IV
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horizons, multiples and VSP
Seismic data showing horizons, multiples and VSP VSP no decon Pri & mult VSP decon Prim only Horizons Multiples
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Zoom-in on multiple and logs
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Seismic gathers showing primaries and multiples
2000 -2000
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Inquisitor Hyperbolic transform High resolution Radon Hybrid Radon
De-Multiple “Fixes” Inquisitor Hyperbolic transform High resolution Radon Hybrid Radon Inverse Scattering Subseries Gabor decon (radial trace domain) Wavelet Transforms Forensic Audiology Discrete Fourier transform target oriented DECON Mapping ** Discussed in this presentation
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Elastic Model gather with
Hyperbolic transform velocity Porosity Multiple
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Dominant Principal Comps
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Principal Comps recombined
Original De-Multipled
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Zone specific DFT Amp Phase Multiple contaminated Primaries
Primary Dominant Freq Multiple Dominant Freq Multiple contaminated Primaries Phase
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Map of first Dominant Frequency
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Target specific Gap decon
Original Section Original De-multipled De-Multipled Section
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Multiple Contaminant risk map
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Conclusions Higher order Interbed multiples can mask potential reservoir properties and create false anomolies. Application of “blind” transform de-multiple can be dangerous – Must be QCed. “Q” can hinder the interpretation and should be considered. Correction for multiples and Q can help high grade prospect risk and improve success rate.
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Acknowledgements ARCIS Corp and Pulse data Inc. for permission to use there data. Schlumberger for permission to use there seismic data Our partners … For there support, discussion and permission to publish. Rick Kuzmiski (GEDCO) for his input and processing of the VSP. Rob Duthie for his input and processing of VSP and his contribution of the VSP explanation slides. Apoterra Seismic and Spectrum Seismic for there patience in the learning and input into the seismic processing. Andy Gamp (ACC) for some of the Multiple Modeling.
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References Goodway, W., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lame’s petrophysical parameters, CSEG Recorder, 22, No7, 3-5. Li, Y.Y., Goodway, W., and Downton, J., 2003, Recent advances in application of AVO to carbonate reservoirs: case histories, CSEG Convention abstracts. Rafavich, F., Kendall, C. H. St. C., and Todd, T. P., 1984, The relationship between acoustic properties and the petrographic character of carbonate rocks, Geophysics, 49, Rio, P, Mukerji, T., Mavko, G., Marion, D., 1996, Velocity dispersion and upscaling in a laboratory-simulated VSP, Geophysics, 61, Tonn, R., 1991, the determination of seismic quality factor Q from VSP data: A comparison of different computational methods. Geophysical Prospecting, 39, 1-27.
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