Agreement DE-FC26-02NT15342, Seismic Evaluation of ...

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Figure 108. Synthetic seismic response generated with Zoeppritz approximation, and a 30Hz Ricker wavelet and a 45 degree offset was used in Hampson-Russell software. The equation assumes brine saturation and the red box corresponds to the lowest depth interval on the well log (3.75 to 3.78 km). (Adapted from Baker et al., 2005) However, the complete response can be modeled using propagator matrix methods (Aki & Richards, 2002). Because this approach enforces all of the required boundary conditions at each interface between the numerous layers, the solution includes all wave propagation phenomena and is not restricted by any assumptions of weak contrasts in material properties or near vertical propagation. This composite reflection coefficient includes the superposition, or "tuning", of all waves reverberating in the model layer (Gibson, 2004). As an example, we show the results of computing the P-wave composite reflection coefficient for the 3.75 to 3.78 km interval in figure 104. The total unit thickness, 0.03 km, is relatively thin compared to a wavelength for frequencies typical of surface seismic data, so reflections from it will be comprised of a single tuned event and it is reasonable to characterize it using the single value of the composite reflection coefficient. Because the reflecting zone has a finite thickness, this coefficient is frequency dependent (Figure 109). As frequency increases from 15 to 60 Hz, the magnitude of the reflected signal increases, and the rate of decrease with an increasing angle of incidence also changes. This suggests that conventional AVO parameters, such as the gradient of the amplitude as a function of the squared sine of incident angle, will be sensitive to frequency. Agreement DE-FC26-02NT15342, Seismic Evaluation of Hydrocarbon Saturation 128
Figure 109. Composite reflection coefficient generated for interval (3.75 to 3.78 km) for three frequencies. (Adapted from Baker et. al, 2005) Modeling of seismic composite reflection coefficients provides important insights into the use of seismic amplitudes for hydrocarbon detection in at least two ways. First, comparisons of results for different spatial correlations shows that the “smoother” the velocity profile (e.g., the Gaussian correlation), the smaller the amount of scatter in reflection amplitudes, especially at near offsets. Geologic environments such as turbidite flows are often important for the exploration and production of hydrocarbons in deep water sites, and these settings often will vary laterally because of different patterns of flow of sands and shales. Therefore, seismic measurements will be affected by different vertical velocity structures at different positions within the reservoir, even for reflections from the “same” reservoir. Our results show that this will introduce scatter, or uncertainty, into AVO results that might be associated with the type of geologic heterogeneity, which may be useful information. Secondly, that comparison of composite coefficients for different fluid contents helps to show how such uncertainty associate purely with lithologic variations might hinder hydrocarbon detection. Model calculations can help to determine whether scatter in AVO measurements will be large compared to the anomalies associated with hydrocarbons, in which case an AVO hydrocarbon indicator might not be effective. The seismic turbidite models (STMs) that we developed based on well measurements can be used to apply the same ideas to a more geologically based model. In our approach, Agreement DE-FC26-02NT15342, Seismic Evaluation of Hydrocarbon Saturation 129
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June 29, 2006 Seismic Evaluation of
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Abstract: During this last quarter
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Ursa Data .........................
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Figures: Figure 1. Relationship of
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Figure 38. Schematic of geometry an
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Figure 73. The extracted wavelet an
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Executive Summary: In this joint pr
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Results and Discussion Introduction
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Inversion of Sw and porosity from s
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saturation from seismic data. Valid
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The Lagrange function (5) has a sad
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ρ ma − ρ + ( ρ w − ρ g ) ×
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With a water saturation log calcula
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Figure 11. permeability versus poro
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Figure 13. Measured dry and brine s
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Figure 15. The HP (deep) sands have
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pressure-porosity relations. The n
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Figure 19. measure dry and brine sa
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Relations between Sw, porosity, and
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Figure 21. An example realization o
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otherwise the seismic properties we
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correctly reproduce those found in
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study of field data from the Teal S
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Figure 30. Spectral decomposed pres
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(Adapted from CSM Slope and Basin C
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Figure 34. Sequence stratigraphic f
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Figure 36. Schematic depiction of c
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Figure 38. Schematic of geometry an
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Figure 40. Middle slope channel (Ad
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Figure 43. Amalgamated sandstone sh
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CHANNEL AXIS SINGLE STORY CHANNEL C
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MEDIAL LEVEE/OVERBANK 7.62 cm Figur
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L1054 L1041 • Ursa Well A4 Green=
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1.9 miles 37 ft Fizz 19 ft Oil 4.0
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esults in shifts followed by subsid
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Figure 55. Schematic of depositiona
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4 miles 1.5 s 2 s 3 s Hydrocarbon I
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In addition to comparing calculated
Page 77 and 78: Figure 61. Shear velocity trend for
Page 79 and 80: each reservoir interval modeled. Th
Page 81 and 82: Figure 65. Histograms of frequency
Page 83 and 84: the pressure trends were analyzed,
Page 85 and 86: P Impedance Model Synthetic 2 -D Mo
Page 87 and 88: Above Magenta reservoir model The A
Page 89 and 90: Figure 73. The extracted wavelet an
Page 91 and 92: saturation and 50% gas saturation.
Page 93 and 94: Figure 77. The amalgamated channel
Page 95 and 96: Figure 79. Channel complex model 2
Page 97 and 98: Figure 81. AVO stack and gathers fo
Page 99 and 100: Figure 82. Log signature includes g
Page 101 and 102: Figure 85. Extracted wavelet and mo
Page 103 and 104: Figure 87. Amalgamated sheet sand m
Page 105 and 106: Figure 89. Pseudo logs, wavelet, sy
Page 107 and 108: Figure 90. Medial basin floor chann
Page 109 and 110: Figure 92. Pseudo logs, wavelet, sy
Page 111 and 112: Figure 94. The post stack seismic e
Page 113 and 114: Figure 96. Statistically extracted
Page 115 and 116: Figure 98. The proximal levee overb
Page 117 and 118: Figure 99. The response of the prox
Page 119 and 120: Figure 101. The pseudo log, wavelet
Page 121 and 122: Figure 102. The distal levee overba
Page 123 and 124: Figure 104. The complex well log ba
Page 125 and 126: then scaled to the model data by mu
Page 127: Figure 107. The Ursa log displays g
Page 131 and 132: Conclusions One of the main conclus
Page 133 and 134: Table 5 Summary of the model facies
Page 135 and 136: REFERENCES Alford, R. M., Kelly, K.
Page 137 and 138: Gardner, M. H., Anderson, D. A., Bo
Page 139 and 140: Mahaffie, M.J., 1994, Reservoir cla
Page 141 and 142: Rothman, D.H., Grotzinger, J.P., Fl
Page 143 and 144: Vail, P., 1977, Seismic Stratigraph
Page 145 and 146: downlap (Mtchum, 1977): A base-disc
Page 147 and 148: direction and downlap onto the tran
Page 149 and 150: Agreement DE-FC26-02NT15342, Seismi
Page 159 and 160: APPENDIX E: VALUES USED FOR RESERVO
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