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Evaluating marine gas-hydrate systems Part I: stochastic rock-physics models for electrical resistivity and seismic velocities of hydrate-bearing sediments

DIANA SAVA,  BOB HARDAGE
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Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78713, U.S.A.,
JSE 2010, 19(4), 371–393;
Submitted: 12 June 2010 | Accepted: 1 September 2010 | Published: 1 October 2010
© 2010 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Sava, D. and Hardage, B.A., 2010. Evaluating marine gas-hydrate systems. Part I: Stochastic rock-physics models for electrical resistivity and seismic velocities of hydrate-bearing sediments. Journal of Seismic Exploration, 19: 371-386. There is an increased need for investigating marine gas-hydrate systems to estimate the magnitude of the energy resource represented by the hydrate and to identify any unstable seafloor conditions that may result from hydrate dissociation, which can jeopardize drilling activities. Deep-water gas-hydrate systems can be studied on large scales with geophysical techniques, such as seismic and electrical surveys. To evaluate near-seafloor gas-hydrate environments we first need to build rock-physics quantitative relations between measurable parameters, such as elastic and electrical properties of sediments containing hydrates, and gas-hydrate saturation. In this study we assume a model of isotropic, load-bearing hydrates, uniformly distributed in the near-seafloor sediments. This Part I of a 2-paper series presents a method for stochastic joint modeling of elastic properties and electrical resistivity of gas-hydrate sediments. The petrophysical parameters involved in the modeling are difficult to estimate and are uncertain. Therefore, probability distribution functions (PDFs) are used to account for the uncertainty associated with each of the petrophysical quantities involved in the modeling. Both electrical resistivity and seismic velocities depend on porosity of the sediments and hydrate concentration, and we refer to them as common model parameters. A Monte Carlo procedure is used to draw values for these common parameters from their associated PDFs and then compute the corresponding velocity and electrical resistivity values using Monte Carlo draws from the PDFs for each of the petrophysical parameters that are required for elastic modeling and for Archie equation for electrical resistivity. The outcome of this procedure is represented by many Monte Carlo realizations that jointly relate hydrate concentration, resistivity, and seismic propagation velocity. This joint relation varies with depth and it is non-unique and uncertain due to variability of the input parameters. These theoretical relations can then be used to estimate hydrate concentration in Green Canyon Gulf of Mexico through a joint inversion technique presented in the Part II.

Keywords
gas hydrates
rock-physics
elastic properties
modeling
Gulf of Mexico
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  54. Zimmer, M.A., 2003. Controls on the Seismic Velocities of Unconsolidated Sands: Measurementsof Pressure, Porosity and Compaction Effects. Ph.D. thesis, Stanford University.SUBJECT INDEX, Volume 19, 2010absorbing boundary conditions 1, 2, 9, 18, 19, 37, 122, 130, 175, 185, 186absorption 1, 2, 4, 9, 12, 13, 18, 104, 119, 185acoustic modeling 19, 174, 182, 185adaptive mesh refinement 122-124, 127, 135, 136aliasing 280, 282-284, 290, 295-297, 299, 301, 322, 340, 343anisotropy 3-5, 19, 22, 23, 32, 37, 40, 44, 45, 53, 58, 64-69, 188, 189, 200, 202,203, 223-225, 227, 349, 350, 354, 356, 358, 359, 364, 365, 370, 371,azimuthal anisotropy 188, 189, 200, 202basis pursuit 304, 307, 320Biot/squirt mechanism 1, 4, 19boundary conditions 1-3, 9, 14, 15, 18, 19, 22, 37, 122, 130, 175, 185, 186, 208,Bowers equation 142, 150-152, 154, 157carbonate reservoir 65, 86, 142channel analysis 162, 163conjugate gradients 321, 322, 326, 329, 335, 345, 346, 348crack density 43-48, 51-54, 57-65, 67damped least-squares 322data regularization 321, 322deep-water 264, 371-373, 375, 383density 4, 8, 24, 28, 43-49, 51-55, 57-65, 67, 78, 79, 85, 89, 128, 130, 142,143-145, 150, 152, 153, 157, 176, 178, 210, 232, 235, 236, 237-242,244-247, 249, 251-253, 255, 256, 273, 284, 285, 378, 381difference image 280, 292dipole sonic logs 87, 88, 92, 96, 99-102, 236effective pressure 142, 143, 150-154, 157, 373, 375, 377, 378, 380, 381elastic properties 45, 47, 50, 56, 58, 59, 66-69, 237, 350, 371, 377, 378, 379-381,elastic tensor 44, 58EMD 161-166, 169-172error function 249, 253, 254, 257, 259-262finite-difference 1, 3, 6, 7, 18, 19, 22, 23, 40, 41, 69, 136, 174, 176, 177, 178,185, 186, 208-214, 216, 218, 220-222, 225, 226, 282, 283, 284, 286fluids 5, 40, 70, 72, 79, 85, 141, 378, 384fractured 45-48, 67-70, 72, 76, 86, 371gas hydrates 271, 279, 371-373, 377, 384, 385
  55. Gaussian 70, 73, 75, 76, 85, 146, 147, 178, 240, 249-251, 253, 255, 256, 257-261,273, 326, 380, 381Gaussian kernel function 70geological pattern 304geostatistics 142, 159
  56. Gulf of Mexico 264, 265, 279, 371, 372, 374, 376, 378, 380, 382-384heavy oil 88, 96, 100-102, 158, 231, 232, 236, 248high-resolution algorithm 122hydrate 263-266, 269, 271-279, 371-374, 376-386image ray 187, 188, 192-194, 197, 205instantaneous frequency 161-171, 238, 241internal multiples 103-109, 111, 114, 117, 120, 131interpolation 24-26, 41, 42, 145, 148, 302, 308, 322, 323, 336, 347, 348inverse scattering 103-105, 117, 120, 121inversion 44, 45, 57, 65, 78, 104-106, 120-122, 143, 158, 203, 208, 226, 232, 235,238-241, 247, 264, 265, 271-275, 277, 278, 304, 305-307, 311-313,315, 320, 321, 324, 327, 332, 322, 323, 326, 329, 335, 347, 348,370, 371, 384, 385kriging 87, 88, 101, 102, 142, 145-148, 154, 155linearized methods 349, 350matching pursuit 85, 86, 304, 306-309, 311, 320, 321modeling 1-4, 9, 10, 18, 19, 40, 41, 44, 45, 67, 68, 110, 121, 122, 134, 135, 160,174-178, 182, 185, 186, 209, 226, 227, 273, 277, 279, 284, 287, 297,301, 302, 321, 348, 349, 367, 371, 372, 377, 380, 382-385modified NAD algorithm 21, 22, 24multi-azimuth surveys 188nearly perfectly matched layer 174, 175, 185neural network 102, 231, 232, 238, 240, 241, 245, 247, 248NMO ellipse 188, 189, 194, 198, 205, 206NMO-stretch effect 280, 295, 298, 299, 301numerical dispersion 21-24, 26-29, 31-34, 40, 122, 131, 133, 208-210, 215, 217,218, 221-223, 225, 226numerical modeling 1-3, 9, 19, 122, 135, 175, 176, 227Nyquist sampling theorem 280, 282-284ocean-bottom-cable 264P-P 264, 265, 267-271, 277, 278P-SV 19, 41, 186, 264, 265, 267-270, 277, 278pore pressure prediction 141-145, 148, 156, 157, 159, 160poroelastic media 1, 3, 9, 17, 19, 185, 186, 227probability density 249, 251-253, 273random heterogeneous media 280-282, 284, 299, 302ray tracing 194, 198, 350-352, 361, 363, 364, 367-369, 371reflectivity 87-90, 92-96, 98, 99, 102, 239, 241, 242, 265, 267, 279, 304, 305-307,311-313, 315, 321, 327regularization 304-306, 327, 321-324, 329, 338, 343-345, 347, 348rock-physics 273, 371, 372, 377, 380, 383, 385, 386sandstone reservoir 70scattering 55, 66, 68, 103-105, 117, 120, 121, 224, 225, 280-282, 284, 287, 290,293, 295, 300-302sign-bit data 249, 250sparse spike inversion 304spatial sampling 23, 28, 280, 282-285, 288-291, 295-297, 299-302, 323spectral attenuation 70, 72, 85stacking velocity 141, 142, 144, 145, 148, 158staggered-grid 1, 3, 6, 7, 18, 19, 23, 174, 176-178, 186thickness variation 161-163, 166, 168, 170, 171thin bed 87, 161, 162, 168-171, 308time migration 141, 144, 145, 187-189, 192-198, 200, 202, 203, 205, 206, 235, 302time-lapse 280, 300, 302transmission losses 104, 106, 110, 111, 113, 115-117, 120travel times 122, 271, 349, 350, 366-370truncation artifact 280, 296, 297, 299, 301TTI media 208-211, 215-224, 226unsplit convolutional perfectly matched layer 19, 174, 175, 185variance 145, 249, 252, 256variography 142, 145, 146velocity analysis 142, 144, 148, 187-189, 195, 197, 201, 203, 271, 276, 279, 338,379, 384, 386
  57. V,/Vs 67, 87, 88, 96, 99-103, 231, 232, 235-248, 264, 269, 271, 377wave equation redatum 322wave equation statics 322wave propagation 1, 3, 5, 9, 17-19, 21-23, 26, 40, 41, 67, 69, 121-124, 128-130,133-139, 174, 176, 178-181, 185, 186, 208, 209-211, 215, 216, 222,223, 226, 227, 281, 301, 302, 323, 341, 349, 350, 352wavefield simulation 22, 32, 41, 208, 225wavelets 71, 87, 88, 91, 92, 95, 96, 99, 101-103, 123, 136, 287, 295, 296, 320weighted-averaging 208-219, 221-223, 226, 229weighting coefficients 208, 211-218Wigner-Ville distribution 70, 71, 86JOURNAL OFSEISMIC EXPLORATIONVolume 19Number 1, January 2010J. Chen, R.P. Bording, E. LiuZ. Zhang and J. BadalD. Yang, G. Song and J. ZhangY. Hu and G.A. McMechanX. Wu And T. LiuL.R. Lines, P.F. Daleyand L. Ibna-HamidCONTENTSThe application of the nearlyoptimal sponge boundary conditionsfor seismic wave propagationin poroelastic media .........A modified NAD algorithm withminimum numerical dispersionfor simulation of anisotropicwave propagation ...........Theoretical elastic stiffnesstensor models at high crack
  58. Oc CSS a) Uh aerate nesceeraseeepeerdar eer geestareertersetaetearaerAnalysis of seismic spectralattenuation based on Wigner-Villedistribution for sandstonereservoir characterization- a case study from West SichuanDepression, China...........The accuracy of dipole soniclogs and its implication forseismic interpretation .........Number 2, April 2010J.E.M. Lira, K.A. Innanen,A.B. Weglein andA.C. RamirezT. Mi, J. Ma, H. Chaurisand H. YangE. Nosrat, A. Javaherian,M.R. Torabi and H.B. AsiriY. Zhou, W. Chen, J. Gaoand Y. HeJ. Chen, C. Zhangand R.P. BordingW. Sollner, I. Tsvankinand E. Filpo Ferreira da SilvaNumber 3, July 2010G. Wu, K. Liang and X. YinC.C. Dumitrescu and L. LinesCorrection of primary amplitudesfor plane-wave transmission lossthrough an acoustic or absorptiveoverburden with the inversescattering series internal multipleattenuation algorithm: an initialstudy and 1D numerical examplesMultilevel adaptive mesh modelingfor wave propagation in layeredmedia ea eratePore pressure prediction using3D seismic velocity data: a casestudy, a carbonate oil field,WIITamE tarseettoeaea rece dattecsdetearaaeicoadtEmpirical mode decompositionbased instantaneous frequencyand seismic thin-bed analysis ....Comparison between the nearlyperfectly matched layer andunsplit convolutional perfectlymatched layer methods usingacoustic wave modeling .......Multi-azimuth prestack timemigration for anisotropic, weaklyheterogeneous media .........Frequency-domain weighted-averaging finite-differencenumerical simulation of qP wavepropagation in TTI media ......Integrated characterization ofheavy oil reservoir using V,/Vratio and neural network analysis. 103. 231L.M. Houston, G.A. Glassand A.D. DymnikovM.V. DeAngelo, D.C. Sava,B.A. Hardage and P.E. MurrayJ. Matsushima and O. NishizawaNumber 4, October 2010T. Nguyen and J. CastagnaD.R. Smith, M.K. Senand R.J. FergusonP.F. Daley, E.S. Krebesand L.R. LinesD. Sava and B.A. HardageSign-bit amplitude recovery inGaussian noise... ..........Integrated 2D 4-C OBC analysisfor estimating hydrate concentra-tions, Green Canyon,Gulf of Mexico ............Difference image of seismicreflection sections with highlydense spatial sampling in randomheterogeneous mediaHigh-resolution reflectivity1DVETSIOI iets rateeere teetaData regularization and datumingby conjugate gradients ........Travel times in TI media:a comparison of exact,approximate and linearizedTetphgdlSs 让 和 二 让 和 二Evaluating marine gas-hydratesystems.Part I: Stochastic rock-physicsmodels for electrical resistivityand seismic velocities of hydrate-bearing sedimentsSubject Index Vol. 19, 2010Contents Vol. 19, 2010
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