Facies modelling for hydrocarbon reservoirs using a combined Markov chain method

References

• Abdelmaksoud A, Amin AT, El-Habaak GH, Ewida HF. 2019. Facies and petrophysical modeling of the Upper Bahariya Member in Abu Gharadig oil and gas field, north Western Desert, Egypt. J Afr Earth Sci. 149:503–516. jafrearsci.2018.09.011
   Web of Science ®Google Scholar
• Abdideh M, Bargahi D. 2012. Designing a 3D model for the prediction of the top of formation in oil fields using geostatistical methods. Geocarto Int. 27(7):569–579.
   Web of Science ®Google Scholar
• Alabert FG, Massonnat GJ. 1990. September. Heterogeneity in a complex turbiditic reservoir: stochastic modelling of facies and petrophysical variability. SPE Annual Technical Conference and Exhibition. OnePetro.
   Google Scholar
• Al-Mudhafar WJ. 2017. Geostatistical lithofacies modeling of the upper sandstone member/Zubair formation in south Rumaila oil field, Iraq. Arab J Geosci. 10(6):153.
   Web of Science ®Google Scholar
• Al-Mudhafar WJ. 2018. Multiple–point geostatistical lithofacies simulation of fluvial sand–rich depositional environment: a case study from Zubair formation/South Rumaila oil field. SPE Reserv Eval Eng. 21(01):39–53.
   Google Scholar
• Armstrong M. 1998. Basic linear geostatistics. Berlin/Heidelberg, Germany: Springer Science & Business Media; p. 15–22.
   Google Scholar
• Ashoori S, Abdideh M, Alavi A. 2016. 3D geostatistical modelling and uncertainty analysis of clay minerals distribution in reservoir rocks. Geocarto Int. 31(3):241–255.
   Web of Science ®Google Scholar
• Baninajar E, Sharghi Y, Mariethoz G. 2019. MPS-APO: a rapid and automatic parameter optimizer for multiple-point geostatistics. Stoch Environ Res Risk Assess. 33(11–12):1969–1989.
   Web of Science ®Google Scholar
• Burgess PM. 2016. Identifying ideal stratigraphic cycles using a quantitative optimization method. Geology. 44(6):443–446.
   Web of Science ®Google Scholar
• Carle SF. 2007. UCRL-SM-232880. T-PROGS: transition probability geostatistical software version 2.1. Livermore (CA): Lawrence Livermore National Laboratory.
   Google Scholar
• Carle SF, Fogg GE. 1996. Transition probability-based indicator geostatistics. Math Geol. 28(4):453–476.
   Google Scholar
• Carle SF, Fogg GE. 2020. Integration of soft data into geostatistical simulation of categorical variables. Front Earth Sci. 8:565707.
   Web of Science ®Google Scholar
• Carle SF. 1999. T-PROGS: transition probability geostatistical software. Davis (CA): University of California; p. 84.
   Google Scholar
• Carle SF. 2000. Use of a transition probability/Markov approach to improve geostatistical of facies architecture (No. UCRL-JC-141551). Livermore (CA): Lawrence Livermore National Lab.
   Google Scholar
• Deng ZP, Jiang SH, Niu JT, Pan M, Liu LL. 2020. Stratigraphic uncertainty characterization using generalized coupled Markov chain. Bull Eng Geol Environ. 79(10):5061–5078.
   Web of Science ®Google Scholar
• Deutsch CV, Journel AG. 1992. Geostatistical software library and user’s guide. Vol. 119. Oxford: Oxford University Press.
   Google Scholar
• Deutsch CV. 2006. A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Comput Geosci. 32(10):1669–1681.
   Web of Science ®Google Scholar
• Elfeki A, Dekking M. 2001. A Markov chain model for subsurface characterization: theory and applications. Mathe Geol. 33(5):569–589.
   Google Scholar
• Elfeki AM, Dekking FM. 2007. Reducing geological uncertainty by conditioning on boreholes: the coupled Markov chain approach. Hydrogeol J. 15(8):1439–1455.
   Web of Science ®Google Scholar
• Elfeki AMM, Dekking FM. 2005. Modeling subsurface heterogeneity by coupled Markov chains: directional dependency, Walther’s law and entropy. Geotech Geol Eng. 23(6):721–756.
   Google Scholar
• Han WS, Kim KY, Choung S, Jeong J, Jung NH, Park E. 2014. Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO2 sequestration sites. Environ Earth Sci. 71(6):2739–2752.
   Web of Science ®Google Scholar
• Jones NL, Walker JR, Carle SF. 2002. Using transition probability geostatistics with MODFLOW. Acta-Univ Carolinae Geol. 46(2/3):295–298.
   Google Scholar
• Journel AG. 1990. September. Geostatistics for reservoir characterization. SPE Annual Technical Conference and Exhibition. OnePetro.
   Google Scholar
• Kamali MR, Omidvar A, Kazemzadeh E. 2013. 3D geostatistical modeling and uncertainty analysis in a carbonate reservoir, SW Iran. J Geol Res. 2013:1–7.
   Google Scholar
• Krumbein WC. 1967. FORTRAN IV computer programs for Markov chain experiments in geology. Evanston (IL): Northwestern University Evanston Ill Department of Geology.
   Google Scholar
• Leuangthong O, McLennan JA, Deutsch CV. 2004. Minimum acceptance criteria for geostatistical realizations. Nat Resour Res. 13(3):131–141. NARR.0000046916.91703.bb.
   Google Scholar
• Li W. 1999. 2-D stochastic simulation of spatial distribution of soil layers and types using the coupled Markov-chain method. Postdoctoral Res. Rep. No. 1. Institute for Land and Water Management, K.U. Leuven. Leuven, Belgium.
   Google Scholar
• Li W, Zhang C. 2006. A generalized Markov chain approach for conditional simulation of categorical variables from grid samples. Tran GIS. 10(4):651–669.
   Google Scholar
• Li W, Zhang C. 2010. Linear interpolation and joint model fitting of experimental transiograms for Markov chain simulation of categorical spatial variables. Int J Geogr Inform Sci. 24(6):821–839.
   Web of Science ®Google Scholar
• Li W. 2007. Transiograms for characterizing spatial variability of soil classes. Soil Sci Soc Am J. 71(3):881–893.
   Web of Science ®Google Scholar
• Li W, Zhang C, Burt JE, Zhu AX, Feyen J. 2004. Two-dimensional Markov chain simulation of soil type spatial distribution. Soil Sci Soc Am J. 68(5):1479–1490.
   Web of Science ®Google Scholar
• Mahmoudi M, Khojasteh ER, Sharghi Y. 2019. Geostatistical modeling of the subsurface geological-geotechnical heterogeneities in the Tabriz Subway, East Azarbayjan Province, Iran. Zeits Deut Gesells Geowissens. 170(2):145–159.
   Web of Science ®Google Scholar
• Moon Y, Zhang YS, Song Y, Park E, Moon HS. 2013. Multivariate statistical analysis and 3D-coupled Markov chain modeling approach for the prediction of subsurface heterogeneity of contaminated soil management in abandoned Guryong Mine Tailings, Korea. Environ Earth Sci. 68(6):1527–1538.
   Web of Science ®Google Scholar
• Mukerji T, Jørstad A, Avseth P, Mavko G, Granli JR. 2001. Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: seismic inversions and statistical rock physics. Geophysics. 66(4):988–1001.
   Web of Science ®Google Scholar
• Park E. 2010. A multidimensional, generalized coupled Markov chain model for surface and subsurface characterization. Water Resour Res. 46(11): DOI: 10.1029/2009WR008355.11509
   PubMedGoogle Scholar
• Park E, Elfeki A, Dekking M. 2005. Characterization of subsurface heterogeneity: integration of soft and hard information using multidimensional coupled Markov chain approach. Dev Water Sci. 52:193–202.
   PubMedGoogle Scholar
• Park E, Elfeki AM, Song Y, Kim K. 2007. Generalized coupled Markov chain model for characterizing categorical variables in soil mapping. Soil Sci Soc Am J. 71(3):909–917.
   Web of Science ®Google Scholar
• Pyrcz MJ, Deutsch CV. 2014. Geostatistical reservoir modeling. Oxford: Oxford University Press.
   Google Scholar
• Qi XH, Li DQ, Phoon KK, Cao ZJ, Tang XS. 2016. Simulation of geologic uncertainty using coupled Markov chain. Eng Geol. 207:129–140.
   Web of Science ®Google Scholar
• Ranjineh Khojasteh E. 2013. Geostatistical three-dimensional modeling of the subsurface unconsolidated materials in the Göttingen area [PhD thesis]. Göttingen Germany: Georg-August-University, School of Science (GAUSS); p. 162.
   Google Scholar
• Remy N, Boucher A, Wu JB. 2008. Applied geostatistics with SGeMS. Cambridge: Cambridge University Press.
   Google Scholar
• Rossi ME, Deutsch CV. 2013. Mineral resource estimation. Berlin/Heidelberg, Germany: Springer Science & Business Media.
   Google Scholar
• Seifert D, Jensen JL. 1999. Using sequential indicator simulation as a tool in reservoir description: issues and uncertainties. Mathe Geol. 31(5):527–550.
   Google Scholar
• Seifert D, Jensen JL. 2000. Object and pixel-based reservoir modeling of a braided fluvial reservoir. Mathe Geol. 32(5):581–603.
   Google Scholar
• Weissmann GS, Carle SF, Fogg GE. 1999. Three-dimensional hydrofacies modeling based on soil surveys and transition probability geostatistics. Water Resour Res. 35(6):1761–1770.
   Web of Science ®Google Scholar
• Zare A, Bagheri M, Ebadi M. 2020. Reservoir facies and porosity modeling using seismic data and well logs by geostatistical simulation in an oil field. Carbonates Evaporites. 35(3):1–10.
   Web of Science ®Google Scholar
• Zhang T. 2008. Incorporating geological conceptual models and interpretations into reservoir modeling using multiple-point geostatistics. Earth Sci Front. 15(1):26–35.
   Google Scholar