AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
4
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

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
Show Less
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: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 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/ )
Download PDF
Cite
XML
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 subdivided
  3. into twelve main formations: Aruma (L-1), Wasia (L-2), Shuaiba (L-3),
  4. Biyadh (L-4), Hith (L-5), Arab (L-6), Hanifa and Tuwaiq Mountain (L-7),
  5. Dhruma (L-8), Marrat (L-9), Minjur (L-10), Jilh (L-11), and Sudair (L-12)
  6. formations. The Paleozoic era consists of five main formations including
  7. Khuff (L-13), Unayzah (L-14), Qusaiba (L-15), Qasim (L-16), and Saq
  8. (L-17) formations. The lowermost layer in our model is the Precambrian
  9. Basement (L-18) that is assumed as a half space with an infinite thickness.
  10. Table 1 shows the average thickness, the lithology, P- and S-wave velocities,
  11. P- and S-wave quality factors, and densities of each Formation in the
  12. Usaylah field model. Eventually, eighteen layers and three major faults were
  13. defined and digitized at varying intervals. Table 2 shows P- and S-wave
  14. velocities, P- and S-wave quality factors, and densities of fluids filling karst
  15. features modeled within the topmost Aruma Formation. The horizons and
  16. faults are presented in Fig. 2.
  17. Table 1. P- and S-wave velocities, P- and S-wave quality factors, and densities of layers
  18. of the Usaylah field model.
  19. Laver Average
  20. yer Formation Thickness Lithology V,(m/s) . ? , V,(m/s) Q Q
  21. No. (m) (kg/m )
  22. L-1 Aruma 160 Limestone 2730.0° 2091.0' 1672.1° 52.25 40.89
  23. L-2 Wasia 230 Sandstone 3233.0° 2277.0° 2141.98 56.86 46.28
  24. L-3 Shuaiba 100 Limestone 3010.0° 2037.07 1817.7' 54.86 42.63
  25. L-4 Biyadh 320 Sandstone 4045.0° 2364.0° 2700.8' 63.60 51.97
  26. L-5 Hith 100 Anhydrite 4483.0° 2960.04 2327.54 66.96 48.24
  27. L-6 Arab 130 Limestone 5140.0° 2400.0° 2748.0' 71.69 52.42
  28. L-7 Hanifa & 310 Limestone 5697.5° 2550.0° 2903.04 75.48 53.88
  29. Tuwaiq : :
  30. Mountain
  31. L-8 Dhruma 341 Limestone 5033.0° 2458.0° 2869.7° 70.94 53.57
  32. L-9 Marrat 146 Shale 3272.0° 2410.0° 1436.0' 57.20 37.89
  33. L-10 Miniur 350 Sandstone 3930.0° 2394.0° 2499.0° 62.69 49.99
  34. L-11 Jilh 293 Dolomite 4823.0° 2400.0° 2760.54 69.45 52.54
  35. L-12 Sudair 100 Shale 5182.08 2372.08 2674.08 71.99 51.71
  36. L-13 Khuff 180 Dolomite 4953.08 2705.5® 2530.0® 70.38 50.30
  37. L-14 Unayzah 100 Sandstone 3752.08 2404.58 2085.08 61.25 45.66
  38. L-15 Qusaiba 300 Shale 3898.08 ”2485.58 2143.08 62.43 46.29
  39. L-16 Qasim 200 Sandstone 3685.0' 2380.0' 2453.0° 60.70 49.53
  40. L-17 Saq 300 Sandstone 3765.0' 2350.0' 2508.0° 61.36 50.08
  41. L-18 Basement ~ Igneous and 6380.0' 2800.0! 3580.0! 79.87 59.83
  42. metamorphic
  43. *Alfaraj et al. (1998); 'Our sandstone eq.; “Ameen et al. (2009) eq.; “Liu et al. (2013); ‘Dasgupta,
  44. et al. (2002); ‘Our shale eq.; Macrides and Kelamis (2000); 'Al-Ahmadi (2009); ‘Mittet (2007),
  45. /Mooney et al. (1985).
  46. Table 2. P- and S-wave velocities, P- and S-wave quality factors, and densities of fluids
  47. filling the karst features within the topmost Aruma formation.
  48. Karst filling | V V, (m/s) | p (kg/m’) Q Q,
  49. (m/s)
  50. Water 1450 0 1000 100,000 | 100,000
  51. Air 330 0 1.23 100,000 | 100,000
  52. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
  53. aa a
  54. Pin,
  55. ~~
  56. -8000
  57. Aruma Wasia Shuaiba Biyadh Hith Arab Hanifa
  58. Dhruma Marrat Minjur Jith 一 一 sudair ——Khuff Unayzah
  59. 一 一 ausaiba aasm ”一 一 saq 一 一 Basement Fault-1 Fault-2 Fault-3
  60. ----- Karsting 1----- Karsting 2 ----- Karsting 3 Karsting 4 Karsting 5
  61. Fig. 2. Digitized model of the Usaylah field in Central Saudi Arabia. Locations of karst
  62. features are indicated by a circle between X=10,000 and 12,000 m.
  63. The elastic properties such as P-wave velocities and densities of each
  64. layer were determined from well-log data available in Alfaraj et al. (1998),
  65. Macrides and Kelamis (2000), Dasgupta et al. (2002), Al-Ahmadi (2009),
  66. and Liu et al. (2013). However, just few S-wave velocities were reported in
  67. all these references. In order to complete the velocities of S-wave for the
  68. whole stratigraphic column, Vs-Vp relations for Saudi Arabian lithologies
  69. were used. For carbonates, we used the relation presented by Ameen et al.
  70. (2009) in eq. (1). For sandstones and shales, we use the relations in eqs. (2)
  71. and (3), respectively, that we developed from well-log data available in
  72. Macrides and Kelamis (2000). P- and S-wave velocities in eqs. (1)-(3) are
  73. expressed in units of m/s
  74. Vs = 0.52Vp + 252.51 (1)
  75. Vs = 0.6882Vp - 83.009 (2)
  76. Vs = 0.4863Vp + 983.74. (3)
  77. P-wave and S-wave quality factors (Qp and Qs) were determined by using
  78. the formula proposed by Mittet (2007). This formula involved taking the
  79. square root of the corresponding value of Vp and Vs, respectively. The
  80. quality factors of karst-filling fluids are assumed to be infinite and were
  81. assigned a large value of 100,000 for modeling purposes. We note that
  82. although Figs. 3(a)-(e) show only the central part of the constructed
  83. viscoelastic models in the Usaylah field, the whole models (Le., 20,000 m
  84. wide by 8,000 m deep) were used for the generation of the synthetic seismic
  85. data sets.
  86. The central Saudi Arabian near-surface layer generally consists of
  87. carbonates that are easily weathered forming karst features that affect
  88. seismic surveys near them. To model these effects, we included five
  89. randomly shaped and closely distributed karst features in the uppermost
  90. Aruma limestone Formation. To add a realistic level of complexity to the
  91. model, two of these karst features are water-saturated while the others are
  92. filled with air. These karst features were absent in one instance of the model
  93. and present in another instance. Fig. 4 shows detailed viscoelastic models of
  94. the karst features.
  95. Z(m)
  96. Fig. 3. (a) Vp model of the Usaylah field in central Saudi Arabia. Color scale shows
  97. velocity values in m/s. (b) Vs model. Color scale shows velocity in m/s. (c) Density
  98. model. Color scale indicates density values in kg/m*.(d) Qp model. Color scale indicates
  99. quality factor values in dimensionless units. (e) Qs model. Color scale indicates quality
  100. factor values in dimensionless units. Karst features are inside the circle of each figure.
  101. X(m) x104
  102. 上 1000
  103. N 5
  104. x10
  105. 14 X(m) 1.15
  107. 生 E
  108. N N50
  110. x104
  111. 1 X(m) :
  112. (9)
  113. Fig. 4. (a) Vp model of the karst features in the topmost Aruma formation. (b) Vs model.
  114. (c) Density model. (d) Qp model. (e) Qs model. Color scales are similar to those used in
  115. Fig. 3.
  116. GENERATION OF SYNTHETIC SEISMIC DATA
  117. After establishing the viscoelastic properties of the two models (with
  118. and without karst features), we decimated them in order to prepare them for
  119. the generation of synthetic seismic data sets using a finite difference method
  120. (FDM). Many FDM parameters depend on the frequency content of the
  121. source wavelet. A zero-phase Ricker wavelet with a peak frequency of 25
  122. Hz was used. The details of the parameters we used are shown in Table 3.
  123. Table 3. Parameters used to generate the synthetic seismic data sets.
  124. Criteria Model without Model with karsting
  125. karsting
  126. Source wavelet 25-Hz zero phase 25-Hz zero phase
  127. Ricker Ricker
  128. Time sampling for FDM calculation 0.2 msec 0.1 msec
  129. Grid size for FDM calculation (dx = 2.5m lm
  130. dz)
  131. Receiver spacing 25m 25m
  132. Shot spacing 50m 50m
  133. Recording time sampling 2 msec 2 msec
  134. Total recording time 6 sec 6 sec
  135. Total number of receivers 801 801
  136. Shots and receivers x-axis 0 to 20,000 m 0 to 20,000 m
  137. Shot and receivers z-axis -15m -15m
  138. Total number of shots 401 shots 401 shots
  139. We made sure the selected FDM parameters satisfied the Courant—
  140. Friedrichs—Lewy (CFL) conditions of dispersion and stability necessary for
  141. the convergence of finite-difference solutions to the wave equation. We use
  142. the fdelmodc source code described in Thorbecke (2016) to generate the
  143. viscoelastic synthetic seismic data sets. The left, right, and bottom
  144. boundaries of the model were absorbing boundaries with a buffer area
  145. consisting of 375 grid cells beyond each of these boundaries. The top
  146. boundary was a free surface, which prompted us to put the sources and
  147. receivers 15 m below it, in order to generate and record seismic data without
  148. encountering ghost-multiple effects.
  149. For each of the above two models (with and without karst features),
  150. two synthetic seismic data sets were generated: vertical and horizontal
  151. components. In order to simulate ambient noise effects, additive Gaussian
  152. random noise with zero mean and 10% standard deviation was added to the
  153. synthetic data sets. Fig. 5 shows sample synthetic seismic records for both
  154. vertical and horizontal component with no karst features, while Fig. 6 shows
  155. the same records in the presence of karst features. The records of Figs. 5 and
  156. 6 have been gained using a time-squared method to enhance visibility of
  157. later arrivals. However, the uploaded digital seismic data sets are raw with
  158. no gain applied.
  159. Receiver Position (m) x104 Receiver Position (m) x104
  160. 0 0.5 1.0 1.5 2.0 0 0.5 1.0 1.5 2.0
  161. Time (s) Time (s)
  162. Fig. 5. (a) Horizontal component with no karst features. (b) Vertical component with no
  163. karst features.
  164. CONCLUSION
  165. We compiled two 2D viscoelastic seismic models (with and without
  166. karst features) of the Usaylah field of central Saudi Arabia and generated
  167. their corresponding multi-component synthetic seismic data sets. The
  168. generated models and synthetic data sets have been made available publicly
  169. over a dedicated online folder, and we invite researchers to test their
  170. algorithms on these data sets and encourage them to share their results
  171. publicly as well. We intend to extend the models to 3D geometry and
  172. include more structural, anisotropic, and fluid effects.
  173. Receiver Position (m) x104 Receiver Position (m) x104
  174. 0 0.5 1.0 1.5 2.0 0 0.5 1.0 1.5 2.0
  175. Time (s)
  176. (a) (b)
  177. Fig. 6. (a) Horizontal component in the presence of karst features. (b) Vertical component
  178. in the presence of karst features. Note the scattering at the karst locations (X = 11,500 m).
  179. ACKNOWLEDGMENTS
  180. This work was funded by MAARIFAH - King Abdulaziz City for
  181. Science and Technology (KACST) — through the Science & Technology
  182. Unit at King Fahd University of Petroleum & Minerals (KFUPM) — the
  183. Kingdom of Saudi Arabia, award number TIC-CCS-1. We thank KACST
  184. and KFUPM for their support.
  185. REFERENCES
  186. Al-Ahmadi, M.E., 2009. Hydrogeology of the Saq aquifer northwest of Tabuk northern
  187. Saudi Arabia. Earth Sci., 20: 51-66.
  188. Alfaraj, M., Nebrija, E.L. and Ferguson, M.D. 1998. The challenge of interpreting 3-D
  189. seismic in Shaybah field Saudi Arabia. GeoArabia, 3: 209-226.
  190. Al-Husseini, M.I., 2004. Pre-Unayzah unconformity, Saudi Arabia. GeoArabia, 3: 15-59.
  191. Al-Khidir, K.E., Al-Quraishi, A.A., Al-Laboun, A.A. and Benzagouta, M.S., 2011.
  192. Bimodal pore size behavior of the Shajara Formation Reservoirs of the Permo-
  193. Carboniferous Unayzah Group, Saudi Arabia. J. Petrol. Explor. Prod. Technol., 1:
  194. 1-9.
  195. Ameen, M.S., Smart, B.G., Somerville, J.M., Hammilton, S. and Naji, N.A., 2009.
  196. Predicting rock mechanical properties of carbonates from wireline logs (A case
  197. study: Arab-D reservoir, Ghawar field, Saudi Arabia). Marine Petrol. Geol., 26:
  198. 430-444.
  199. Dasgupta, S.N., Hong, M.R. and Al-Jallal, LA., 2002. Accurate characterization to reduce
  200. drilling risk in Khuff-C carbonate, Ghawar field, Saudi Arabia. GeoArabia, 7: 81-
  202. Evans, D.S., Bahabri, B.H. and Al-Otaibi, A.M. 1997. Stratigraphic trap in the Permian
  203. Unayzah Formation, Central Saudi Arabia. GeoArabia, 2: 260-278.
  204. Fournier, F., Dequirez, P.Y., Macridesz, C.G. and Rademakers, M., 2002. Quantitative
  205. lithostratigraphic interpretation of seismic data for characterization of the
  206. Unayzah Formation in central Saudi Arabia. Geophysics, 67: 1372-1381.
  207. Knox, R.W., Cocker, J.D. and Filatoff, J.D., 2010. Heavy mineral stratigraphy of the
  208. Unayzah Formation and Basal Khuff Clastics (Carboniferous to Permian) of
  209. Central Saudi Arabia. GeoArabia - Middle East Petrol. Geosci., 15(3): 17-80.
  210. Liu, L., Kharji, M. and Chatterjee, H.S., 2013. Seismic attributes as exploration tools: A
  211. case study from the Jurassic in Saudi Arabia. IPTC 2013.
  212. Macrides, C.G. and Kelamis, P.G., 2000. 9C-2D Land seismic experiment for lithology
  213. estimation of a Permian clastic reservoir. GeoArabia, 5: 427-440.
  214. McGillivray, J.G. and Husseini, M.I., 1992. The Paleozoic petroleum geology of central
  215. Arabia. AAPG Bull., 76: 1473-1490.
  216. Melvin, J. and Sprague, A., 2006. Origin and stratigraphic architecture of galciogenic
  217. sediments in Permian-Carboniferous Lower Unayzah Sandstones, Eastern Central
  218. Saudi Arabia. GeoArabia 11(4): 105-152.
  219. Mittet, R., 2007. A simple design procedure for depth extrapolation operators that
  220. compensate for absorption and dispersion. Geophysics, 72(2): S105-S112.
  221. Mooney, W.D., Gettings, M.E., Blank, H.R. and Healy, J.H., 1985. Saudi Arabian
  222. seismic-refraction profile: A traveltime interpretation of crustal and upper mantle
  223. structure. Tectonophysics, 111: 173-246.
  224. Thorbecke, J. 2016. 2D Finite-difference wavefield modeling. Accessed 13 Dec. 2017.
  225. https://janth.home.xs4all.nl/Software/fdelmodcManual.pdf.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept