Permeability Prediction and Validation Using Static and Dynamic Data of Sandstone Reservoir in Weizhou Oilfield, Beibuwan Basin, China
DOI:
https://doi.org/10.15377/2409-787X.2021.08.1Keywords:
permeability; well log interpretation; dynamic data; Liushagang Formation; Weizhou OilfieldAbstract
Permeability is one of the key parameters in reservoir property studies. The existing well log interpretation models could not predict the permeability accurately due to the complexity and ambiguity of well logging curves, and the prediction results may demonstrate significant contradictions with the production data. Based on the comprehensive analysis of cores, well logs, laboratory tests, and thin section observations, we take the first member of Liushagang Formation (L1) in Weizhou 11-1N Oil Field as the target, and select median grain size, porosity, and resistivity to establish a multiple nonlinear regression interpretation model of permeability. The accuracy and applicability of this model is validated by the laboratory test data and oil production performance. This permeability interpretation model is easy and practical to operate. Furthermore, it bridges the geological characteristics and the production performance.
Downloads
References
Wyckoff, R.D, Botset, H.G., Muskat, M., 1933. The measurement of the permeability of porous media for homogeneous fluids. Review of Scientific Instruments, 4(7): 394-405. DOI: https://doi.org/10.1063/1.1749155
Martin, M., Murray, G.H., Gillingham, W.J., 1938. Determination of the potential productivity of oil-bearing formations by resistivity measurements. Geophysics, 3(3): 258-272. DOI: https://doi.org/10.1190/1.1439502
Archie, G.E., 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the AIME, 146(1): 54-62. DOI: https://doi.org/10.2118/942054-G
Brace, W.F., 1977. Permeability from resistivity and pore shape. Journal of Geophysical Research, 82(23): 3343-3349. DOI: https://doi.org/10.1029/JB082i023p03343
Jackson, P.D., Smith, D.T., Stanford, P.N., 1978. Resistivity-porosity-particle shape relationships for marine sands. Geophysics, 43(6): 1250-1268. DOI: https://doi.org/10.1190/1.1440891
Katz, A.J., Thompson, A.H., 1985. Fractal sandstone pores: implications for conductivity and pore formation. Physical Review Letters, 54(12): 1325. DOI: https://doi.org/10.1103/PhysRevLett.54.1325
Katz, A.J., Thompson, A.H., 1986. Quantitative prediction of permeability in porous rock. Physical review, 34(11): 8179. DOI: https://doi.org/10.1103/PhysRevB.34.8179
de Lima, O.A.L., 1995. Water saturation and permeability from resistivity, dielectric, and porosity logs. Geophysics, 60(6): 1756-1764. DOI: https://doi.org/10.1190/1.1443909
Fowles, J., Burley, S., 1994. Textural and permeability characteristics of faulted, high porosity sandstones. Marine and Petroleum Geology, 11(5): 608-623. DOI: https://doi.org/10.1016/0264-8172(94)90071-X
Mohaghegh, S., Balan, B., Ameri, S., 1995. State-of-the-art in permeability determination from well log data: Part 2-verifiable accurate permeability predictions the touch-stone of all models. Society of Petroleum Engineers. DOI: https://doi.org/10.2118/30979-MS
Mohaghegh, S., Arefi, R., Bilgesu, I., 1995. Design and development of an artificial neural network for estimation of formation permeability. SPE Computer Applications, 7(6): 151-154. DOI: https://doi.org/10.2118/28237-PA
Saner, S., M. Kissami, S. AlNufaili. 1997. Estimation of permeability from well logs using resistivity and saturation data. SPE Formation Evaluation, 12(1): 27-31. DOI: https://doi.org/10.2118/26277-PA
Xue, G.P., DattaGupta, A., Valko, P., 1997. Optimal transformations for multiple regression: Application to permeability estimation from well logs. SPE Formation Evaluation, 12(2): 85-93. DOI: https://doi.org/10.2118/35412-PA
Yang, Y., Aplin, A.C., 1998. Influence of lithology and compaction on the pore size distribution and modelled permeability of some mudstones from the Norwegian margin. Marine and Petroleum Geology, 15(2): 163-175. DOI: https://doi.org/10.1016/S0264-8172(98)00008-7
Cuddy, S. J., 2000. Litho-facies and permeability prediction from electrical logs using fuzzy logic. SPE Reservoir Evaluation & Engineering, 3(4): 319-324. DOI: https://doi.org/10.2118/65411-PA
Bhatt, A., Helle, H.B., 2002. Committee neural networks for porosity and permeability prediction from well logs. Geophysical Prospecting, 50(6): 645-660. DOI: https://doi.org/10.1046/j.1365-2478.2002.00346.x
Lee, S.H., Kharghoria, A., Datta-Gupta, A., 2002. Electrofacies characterization and permeability predictions in complex reservoirs. SPE Reservoir Evaluation & Engineering, 5(3): 237-248. DOI: https://doi.org/10.2118/78662-PA
Perez, H.H., Datta-Gupta, A., Mishra, S., 2005. The Role of Electrofacies Lithofacies and Hydraulic Flow Units in Permeability Predictions from Well Logs: A Comparative Analysis Using Classification Trees. SPE Reservoir Evaluation & Engineering, 8(2): 143-155. DOI: https://doi.org/10.2118/84301-PA
Rezaee, M.R., Jafari, A., Kazemzadeh, E., 2006. Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks. Journal of Geophysics and Engineering, 3(4): 370-383. DOI: https://doi.org/10.1088/1742-2132/3/4/008
Shokir, E.M., Alsughayer, A.A., Al-Ateeq, A., 2006. Permeability estimation from well log responses. Journal of Canadian Petroleum Technology, 45(11): 41-46. DOI: https://doi.org/10.2118/06-11-05
Al-Anazi, A., Gates, I.D., 2010. Support-Vector Regression for Permeability Prediction in a Heterogeneous Reservoir: A Comparative Study. SPE Reservoir Evaluation & Engineering, 13(3): 485-495. DOI: https://doi.org/10.2118/126339-PA
Tahar, A., Baouche, R., Baddari, k., 2014. Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R׳Mel gas field, Algeria. Journal of Petroleum Science and Engineering, 123: 217-229. DOI: https://doi.org/10.1016/j.petrol.2014.09.019
Chehrazi, A., Rezaee, R., 2012. A systematic method for permeability prediction, a Petro-Facies approach. Journal of Petroleum Science and Engineering, 82: 1-16. DOI: https://doi.org/10.1016/j.petrol.2011.12.004
Jesús, D.C., Biosca, B., Miguel, M.J., 2015. Geophysical estimation of permeability in sedimentary media with porosities from 0 to 50%. Oil & Gas Science and Technology-Revue d’IFP Energies nouvelles, 71(2): 27-35. DOI: https://doi.org/10.2516/ogst/2014053
Fitch, P.J.R, Lovell, M.A, Davies, S.J., 2015. An integrated and quantitative approach to petrophysical heterogeneity. Marine and Petroleum Geology, 63: 82-96. DOI: https://doi.org/10.1016/j.marpetgeo.2015.02.014
Rosenbrand, E., Fabricius, I.L., Fisher, Q., 2015. Permeability in Rotliegend gas sandstones to gas and brine as predicted from NMR, mercury injection and image analysis. Marine and Petroleum Geology, 64: 189-202. DOI: https://doi.org/10.1016/j.marpetgeo.2015.02.009
Taylor, T.R., Kittridge, M.G., Winefield, P., 2015. Reservoir quality and rock properties modeling - Triassic and Jurassic sandstones, greater Shearwater area, UK Central North Sea. Marine And Petroleum Geology, 65: 1-21. DOI: https://doi.org/10.1016/j.marpetgeo.2015.03.020
Zhu, L.Q., Zhang, C., Wei, Y., Zhang, C.M., 2017. Permeability Prediction of the Tight Sandstone Reservoirs Using Hybrid Intelligent Algorithm and Nuclear Magnetic Resonance Logging Data. Arabian Journal for Science and Engineering, 42(4): 1643-1654. doi:10.1007/s13369-016-2365-2 DOI: https://doi.org/10.1007/s13369-016-2365-2
Ju, W., Wu, C.F., Wang, k., Sun, W.F., Li, C., Chang, X.X., 2017. Prediction of tectonic fractures in low permeability sandstone reservoirs: A case study of the Es-3(m) reservoir in the Block Shishen 100 and adjacent regions, Dongying Depression. Journal of Petroleum Science and Engineering, 156: 884-895. doi:10.1016/j.petrol.2017.06.068 DOI: https://doi.org/10.1016/j.petrol.2017.06.068
Wang, J., Cao, Y.C., Liu, k.Y., Liu, J., Kashif, M., 2017. Identification of sedimentary-diagenetic facies and reservoir porosity and permeability prediction: An example from the Eocene beach-bar sandstone in the Dongying Depression, China. Marine and Petroleum Geology, 82: 69-84. doi:10.1016/j.marpetgeo.2017.02.004 DOI: https://doi.org/10.1016/j.marpetgeo.2017.02.004
Liu, M., Xie, R.H., Wu, S.T., Zhu, R.k., Mao, Z.G., Wang, C.S., 2018. Permeability prediction from mercury injection capillary pressure curves by partial least squares regression method in tight sandstone reservoirs. Journal of Petroleum Science and Engineering, 169: 135-145. doi:10.1016/j.petrol.2018.05.020 DOI: https://doi.org/10.1016/j.petrol.2018.05.020
Ngo, V.T., Lu, V.D., Le, V.M., 2018. A comparison of permeability prediction methods using core analysis data for sandstone and carbonate reservoirs. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 4(2): 129-139. doi:10.1007/s40948-017-0078-y DOI: https://doi.org/10.1007/s40948-017-0078-y
Ojo, S.A., Ozebo, V.C., Olusola, O.I., Olatinsu, O.B., 2018. Continuous permeability predictions in heterogeneous reservoirs using Vshale and microstructure calibrated free-fluid models(a combined study of a Niger Delta field and the tight gas sandstone of the southern North Sea). Arabian Journal of Geosciences, 11(17): 527. doi:10.1007/s12517-018-3879-6 DOI: https://doi.org/10.1007/s12517-018-3879-6
Lis-Sledziona, A., 2019. Petrophysical rock typing and permeability prediction in tight sandstone reservoir. Acta Geophysica, 67(6): 1895-1911. doi:10.1007/s11600-019-00348-5 DOI: https://doi.org/10.1007/s11600-019-00348-5
Rostami, S., Rashidi, F., Safari, H., 2019. Prediction of oil-water relative permeability in sandstone and carbonate reservoir rocks using the CSA-LSSVM algorithm. Journal of Petroleum Science and Engineering, 173: 170-186. doi:10.1016/j.petrol.2018.09.085 DOI: https://doi.org/10.1016/j.petrol.2018.09.085
Baouche, R; Nabawy, BS, 2021. Permeability prediction in argillaceous sandstone reservoirs using fuzzy logic analysis: A case study of triassic sequences, Southern Hassi R'Mel Gas Field, Algeria. Journal of African Earth Sciences, 173: 104049. doi:10.1016/j.jafrearsci.2020.104049 DOI: https://doi.org/10.1016/j.jafrearsci.2020.104049
Zhang, G.Y., Wang, Z.Z., Mohaghegh, S., Lin, C.Y., Sun, Y.N., Pei, S.J., 2021. Pattern visualization and understanding of machine learning models for permeability prediction in tight sandstone reservoirs. Journal of Petroleum Science and Engineering, 200: 108142. doi:10.1016/j.petrol.2020.108142 DOI: https://doi.org/10.1016/j.petrol.2020.108142
Tsakiroglou, C.D., Payatakes, A.C. 2000. Characterization of the pore structure of reservoir rocks with the aid of serial sectioning analysis, mercury porosimetry and network simulation. Advances in Water Resources, 23(7): 773-789. DOI: https://doi.org/10.1016/S0309-1708(00)00002-6
Clarkson, C.R, Solano, N., Bustin, R.M., 2013. Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel, 103: 606-616. DOI: https://doi.org/10.1016/j.fuel.2012.06.119
Simandoux, P., 1963. Dielectric measurements of porous media: Application to measurement of water saturations-study of the behavior of argillaceous formations. Revue de L’institut Francais du Petrole, 18(S1): 193-215.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 International Journal of Petroleum Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.


