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Digital viscoelastic seismic models and data sets of central Saudi Arabia in the presence of near-surface karst features

ABDULLATIF A. AL-SHUHAIL1 ,  ABDULLAH A. ALSHUHAIL1 ,  YEHIA A. KHULIEF2 ,  OLUSEUN A. SANUADE1 ,  AYMAN F. AL-LEHYANI1 ,  SEPTRII A. CHAN1 ,  ABDUL LATIF ASHADI1 ,  MOHAMMED ZIA ULLAH KHAN3 ,  SIKAR KHAN2 ,  ADNAN M. ALMUBARAK1 ,  SALEM G. AL-JUHANI1 ,  SYED ABDUL SALAM3
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1 Geosciences Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia. ashuhail@kfupm.edu.sa,
2 Mechanical Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia.,
3 Electrical Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia.,
JSE 2020, 29(1), 15–28;
Submitted: 19 September 2018 | Accepted: 30 October 2019 | Published: 1 February 2020
© 2020 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

Al-Shuhail, A.A., Alshuhail, A.A., Khulief, Y.A., Sanuade, O.A., Al-Lehyani, A.F., Chan, S.A., Ashadi, A.L., Khan, M.Z.U., Khan, S., Almubarak, A.M., Al-Juhani, S.G. and Salam, S.A., 2020. Digital viscoelastic seismic models and data sets of central Saudi Arabia in the presence of near-surface karst features. Journal of Seismic Exploration, 29: 15-28. Central Saudi Arabian fields are generally known for their high-quality light oil that derives from structural and stratigraphic traps. Despite their unique geological settings, there are no existing digital geological models or synthetic seismic data that are publicly available for testing hypotheses and algorithms. We attempt to fill this gap by compiling 2D viscoelastic models of one of these fields and generate corresponding multi-component synthetic seismic data sets. We selected the Usaylah field because its oil production comes interestingly from a stratigraphic trap in the Permian siliciclastic Unayzah reservoir. Furthermore, to simulate realistic central Arabian near-surface conditions, we generate two models: one with and another without karst features in the top Aruma limestone. The P-wave velocities and densities of the entire stratigraphic column from basement to Aruma limestone were compiled from public sources. We then calculate S-wave velocities from P-wave velocities using empirical Vs-Vp relations that we established and generalized from few published well logs. P-wave and S-wave quality factors were also calculated from the corresponding P-wave and S-wave velocities using an empirical square-root formula. A total of four synthetic 2D seismic data sets comprising the horizontal and vertical components excluding and including karst features were generated using a finite-difference algorithm. We share the models and seismic data sets publicly hoping that this will motivate interested researchers to test their research ideas.

Keywords
viscoelastic model
Usaylah field
karst feature
References
  1. The geologic column in the Usaylah field consists of Mesozoic era,
  2. Paleozoic era, and a Precambrian Basement. Mesozoic era was subdividedinto twelve main formations: Aruma (L-1), Wasia (L-2), Shuaiba (L-3),
  3. Biyadh (L-4), Hith (L-5), Arab (L-6), Hanifa and Tuwaiq Mountain (L-7),
  4. Dhruma (L-8), Marrat (L-9), Minjur (L-10), Jilh (L-11), and Sudair (L-12)formations. The Paleozoic era consists of five main formations including
  5. Khuff (L-13), Unayzah (L-14), Qusaiba (L-15), Qasim (L-16), and Saq(L-17) formations. The lowermost layer in our model is the Precambrian
  6. Basement (L-18) that is assumed as a half space with an infinite thickness.
  7. Table 1 shows the average thickness, the lithology, P- and S-wave velocities,
  8. P- and S-wave quality factors, and densities of each Formation in the
  9. Usaylah field model. Eventually, eighteen layers and three major faults weredefined and digitized at varying intervals. Table 2 shows P- and S-wavevelocities, P- and S-wave quality factors, and densities of fluids filling karstfeatures modeled within the topmost Aruma Formation. The horizons andfaults are presented in Fig. 2.
  10. Table 1. P- and S-wave velocities, P- and S-wave quality factors, and densities of layersof the Usaylah field model.Laver Averageyer Formation Thickness Lithology V,(m/s) . ? , V,(m/s) Q QNo. (m) (kg/m )
  11. L-1 Aruma 160 Limestone 2730.0° 2091.0' 1672.1° 52.25 40.89
  12. L-2 Wasia 230 Sandstone 3233.0° 2277.0° 2141.98 56.86 46.28
  13. L-3 Shuaiba 100 Limestone 3010.0° 2037.07 1817.7' 54.86 42.63
  14. L-4 Biyadh 320 Sandstone 4045.0° 2364.0° 2700.8' 63.60 51.97
  15. L-5 Hith 100 Anhydrite 4483.0° 2960.04 2327.54 66.96 48.24
  16. L-6 Arab 130 Limestone 5140.0° 2400.0° 2748.0' 71.69 52.42
  17. L-7 Hanifa & 310 Limestone 5697.5° 2550.0° 2903.04 75.48 53.88Tuwaiq : :Mountain
  18. L-8 Dhruma 341 Limestone 5033.0° 2458.0° 2869.7° 70.94 53.57
  19. L-9 Marrat 146 Shale 3272.0° 2410.0° 1436.0' 57.20 37.89
  20. L-10 Miniur 350 Sandstone 3930.0° 2394.0° 2499.0° 62.69 49.99
  21. L-11 Jilh 293 Dolomite 4823.0° 2400.0° 2760.54 69.45 52.54
  22. L-12 Sudair 100 Shale 5182.08 2372.08 2674.08 71.99 51.71
  23. L-13 Khuff 180 Dolomite 4953.08 2705.5® 2530.0® 70.38 50.30
  24. L-14 Unayzah 100 Sandstone 3752.08 2404.58 2085.08 61.25 45.66
  25. L-15 Qusaiba 300 Shale 3898.08 ”2485.58 2143.08 62.43 46.29
  26. L-16 Qasim 200 Sandstone 3685.0' 2380.0' 2453.0° 60.70 49.53
  27. L-17 Saq 300 Sandstone 3765.0' 2350.0' 2508.0° 61.36 50.08
  28. L-18 Basement ~ Igneous and 6380.0' 2800.0! 3580.0! 79.87 59.83metamorphic*Alfaraj et al. (1998); 'Our sandstone eq.; “Ameen et al. (2009) eq.; “Liu et al. (2013); ‘Dasgupta,et al. (2002); ‘Our shale eq.; Macrides and Kelamis (2000); 'Al-Ahmadi (2009); ‘Mittet (2007),/Mooney et al. (1985).
  29. Table 2. P- and S-wave velocities, P- and S-wave quality factors, and densities of fluidsfilling the karst features within the topmost Aruma formation.Karst filling | V V, (m/s) | p (kg/m’) Q Q,(m/s)Water 1450 0 1000 100,000 | 100,000Air 330 0 1.23 100,000 | 100,0000 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000aa aPin,~~-8000Aruma Wasia Shuaiba Biyadh Hith Arab Hanifa
  30. Dhruma Marrat Minjur Jith 一 一 sudair ——Khuff Unayzah一 一 ausaiba aasm ”一 一 saq 一 一 Basement Fault-1 Fault-2 Fault-3----- Karsting 1----- Karsting 2 ----- Karsting 3 Karsting 4 Karsting 5
  31. Fig. 2. Digitized model of the Usaylah field in Central Saudi Arabia. Locations of karstfeatures are indicated by a circle between X=10,000 and 12,000 m.
  32. The elastic properties such as P-wave velocities and densities of eachlayer were determined from well-log data available in Alfaraj et al. (1998),
  33. Macrides and Kelamis (2000), Dasgupta et al. (2002), Al-Ahmadi (2009),and Liu et al. (2013). However, just few S-wave velocities were reported inall these references. In order to complete the velocities of S-wave for thewhole stratigraphic column, Vs-Vp relations for Saudi Arabian lithologieswere used. For carbonates, we used the relation presented by Ameen et al.(2009) in eq. (1). For sandstones and shales, we use the relations in eqs. (2)and (3), respectively, that we developed from well-log data available in
  34. Macrides and Kelamis (2000). P- and S-wave velocities in eqs. (1)-(3) areexpressed in units of m/sVs = 0.52Vp + 252.51 (1)Vs = 0.6882Vp - 83.009 (2)Vs = 0.4863Vp + 983.74. (3)
  35. P-wave and S-wave quality factors (Qp and Qs) were determined by usingthe formula proposed by Mittet (2007). This formula involved taking thesquare root of the corresponding value of Vp and Vs, respectively. Thequality factors of karst-filling fluids are assumed to be infinite and wereassigned a large value of 100,000 for modeling purposes. We note thatalthough Figs. 3(a)-(e) show only the central part of the constructedviscoelastic models in the Usaylah field, the whole models (Le., 20,000 mwide by 8,000 m deep) were used for the generation of the synthetic seismicdata sets.
  36. The central Saudi Arabian near-surface layer generally consists ofcarbonates that are easily weathered forming karst features that affectseismic surveys near them. To model these effects, we included fiverandomly shaped and closely distributed karst features in the uppermost
  37. Aruma limestone Formation. To add a realistic level of complexity to themodel, two of these karst features are water-saturated while the others arefilled with air. These karst features were absent in one instance of the modeland present in another instance. Fig. 4 shows detailed viscoelastic models ofthe karst features.Z(m)
  38. Fig. 3. (a) Vp model of the Usaylah field in central Saudi Arabia. Color scale showsvelocity values in m/s. (b) Vs model. Color scale shows velocity in m/s. (c) Densitymodel. Color scale indicates density values in kg/m*.(d) Qp model. Color scale indicatesquality factor values in dimensionless units. (e) Qs model. Color scale indicates qualityfactor values in dimensionless units. Karst features are inside the circle of each figure.X(m) x104上 1000N 5x1014 X(m) 1.15生 EN N50x1041 X(m) :(9)
  39. Fig. 4. (a) Vp model of the karst features in the topmost Aruma formation. (b) Vs model.(c) Density model. (d) Qp model. (e) Qs model. Color scales are similar to those used inFig. 3.GENERATION OF SYNTHETIC SEISMIC DATA
  40. After establishing the viscoelastic properties of the two models (withand without karst features), we decimated them in order to prepare them forthe generation of synthetic seismic data sets using a finite difference method(FDM). Many FDM parameters depend on the frequency content of thesource wavelet. A zero-phase Ricker wavelet with a peak frequency of 25
  41. Hz was used. The details of the parameters we used are shown in Table 3.
  42. Table 3. Parameters used to generate the synthetic seismic data sets.Criteria Model without Model with karstingkarstingSource wavelet 25-Hz zero phase 25-Hz zero phaseRicker Ricker
  43. Time sampling for FDM calculation 0.2 msec 0.1 msecGrid size for FDM calculation (dx = 2.5m lmdz)Receiver spacing 25m 25mShot spacing 50m 50mRecording time sampling 2 msec 2 msecTotal recording time 6 sec 6 secTotal number of receivers 801 801
  44. Shots and receivers x-axis 0 to 20,000 m 0 to 20,000 mShot and receivers z-axis -15m -15mTotal number of shots 401 shots 401 shots
  45. We made sure the selected FDM parameters satisfied the Courant—
  46. Friedrichs—Lewy (CFL) conditions of dispersion and stability necessary forthe convergence of finite-difference solutions to the wave equation. We usethe fdelmodc source code described in Thorbecke (2016) to generate theviscoelastic synthetic seismic data sets. The left, right, and bottomboundaries of the model were absorbing boundaries with a buffer areaconsisting of 375 grid cells beyond each of these boundaries. The topboundary was a free surface, which prompted us to put the sources andreceivers 15 m below it, in order to generate and record seismic data withoutencountering ghost-multiple effects.
  47. For each of the above two models (with and without karst features),two synthetic seismic data sets were generated: vertical and horizontalcomponents. In order to simulate ambient noise effects, additive Gaussianrandom noise with zero mean and 10% standard deviation was added to thesynthetic data sets. Fig. 5 shows sample synthetic seismic records for bothvertical and horizontal component with no karst features, while Fig. 6 showsthe same records in the presence of karst features. The records of Figs. 5 and6 have been gained using a time-squared method to enhance visibility oflater arrivals. However, the uploaded digital seismic data sets are raw withno gain applied.
  48. Receiver Position (m) x104 Receiver Position (m) x1040 0.5 1.0 1.5 2.0 0 0.5 1.0 1.5 2.0Time (s) Time (s)
  49. Fig. 5. (a) Horizontal component with no karst features. (b) Vertical component with nokarst features.CONCLUSION
  50. We compiled two 2D viscoelastic seismic models (with and withoutkarst features) of the Usaylah field of central Saudi Arabia and generatedtheir corresponding multi-component synthetic seismic data sets. Thegenerated models and synthetic data sets have been made available publiclyover a dedicated online folder, and we invite researchers to test theiralgorithms on these data sets and encourage them to share their resultspublicly as well. We intend to extend the models to 3D geometry andinclude more structural, anisotropic, and fluid effects.
  51. Receiver Position (m) x104 Receiver Position (m) x1040 0.5 1.0 1.5 2.0 0 0.5 1.0 1.5 2.0Time (s)(a) (b)
  52. Fig. 6. (a) Horizontal component in the presence of karst features. (b) Vertical componentin the presence of karst features. Note the scattering at the karst locations (X = 11,500 m).ACKNOWLEDGMENTS
  53. This work was funded by MAARIFAH - King Abdulaziz City for
  54. Science and Technology (KACST) — through the Science & Technology
  55. Unit at King Fahd University of Petroleum & Minerals (KFUPM) — the
  56. Kingdom of Saudi Arabia, award number TIC-CCS-1. We thank KACSTand KFUPM for their support.REFERENCES
  57. Al-Ahmadi, M.E., 2009. Hydrogeology of the Saq aquifer northwest of Tabuk northernSaudi Arabia. Earth Sci., 20: 51-66.
  58. Alfaraj, M., Nebrija, E.L. and Ferguson, M.D. 1998. The challenge of interpreting 3-Dseismic in Shaybah field Saudi Arabia. GeoArabia, 3: 209-226.
  59. Al-Husseini, M.I., 2004. Pre-Unayzah unconformity, Saudi Arabia. GeoArabia, 3: 15-59.
  60. Al-Khidir, K.E., Al-Quraishi, A.A., Al-Laboun, A.A. and Benzagouta, M.S., 2011.
  61. Bimodal pore size behavior of the Shajara Formation Reservoirs of the Permo-
  62. Carboniferous Unayzah Group, Saudi Arabia. J. Petrol. Explor. Prod. Technol., 1:1-9.
  63. Ameen, M.S., Smart, B.G., Somerville, J.M., Hammilton, S. and Naji, N.A., 2009.
  64. Predicting rock mechanical properties of carbonates from wireline logs (A casestudy: Arab-D reservoir, Ghawar field, Saudi Arabia). Marine Petrol. Geol., 26:430-444.
  65. Dasgupta, S.N., Hong, M.R. and Al-Jallal, LA., 2002. Accurate characterization to reducedrilling risk in Khuff-C carbonate, Ghawar field, Saudi Arabia. GeoArabia, 7: 81-
  66. Evans, D.S., Bahabri, B.H. and Al-Otaibi, A.M. 1997. Stratigraphic trap in the Permian
  67. Unayzah Formation, Central Saudi Arabia. GeoArabia, 2: 260-278.
  68. Fournier, F., Dequirez, P.Y., Macridesz, C.G. and Rademakers, M., 2002. Quantitativelithostratigraphic interpretation of seismic data for characterization of the
  69. Unayzah Formation in central Saudi Arabia. Geophysics, 67: 1372-1381.
  70. Knox, R.W., Cocker, J.D. and Filatoff, J.D., 2010. Heavy mineral stratigraphy of the
  71. Unayzah Formation and Basal Khuff Clastics (Carboniferous to Permian) of
  72. Central Saudi Arabia. GeoArabia - Middle East Petrol. Geosci., 15(3): 17-80.
  73. Liu, L., Kharji, M. and Chatterjee, H.S., 2013. Seismic attributes as exploration tools: Acase study from the Jurassic in Saudi Arabia. IPTC 2013.
  74. Macrides, C.G. and Kelamis, P.G., 2000. 9C-2D Land seismic experiment for lithologyestimation of a Permian clastic reservoir. GeoArabia, 5: 427-440.
  75. McGillivray, J.G. and Husseini, M.I., 1992. The Paleozoic petroleum geology of centralArabia. AAPG Bull., 76: 1473-1490.
  76. Melvin, J. and Sprague, A., 2006. Origin and stratigraphic architecture of galciogenicsediments in Permian-Carboniferous Lower Unayzah Sandstones, Eastern CentralSaudi Arabia. GeoArabia 11(4): 105-152.
  77. Mittet, R., 2007. A simple design procedure for depth extrapolation operators thatcompensate for absorption and dispersion. Geophysics, 72(2): S105-S112.
  78. Mooney, W.D., Gettings, M.E., Blank, H.R. and Healy, J.H., 1985. Saudi Arabianseismic-refraction profile: A traveltime interpretation of crustal and upper mantlestructure. Tectonophysics, 111: 173-246.
  79. Thorbecke, J. 2016. 2D Finite-difference wavefield modeling. Accessed 13 Dec. 2017.https://janth.home.xs4all.nl/Software/fdelmodcManual.pdf.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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