Abbreviations
- GEP:
-
Gene expression programming
- GA:
-
Genetic algorithm
- GP:
-
Genetic programming
- MLPN:
-
Feed forward multilayer perceptron network
- RMSE:
-
Root mean squared error
- R 2 :
-
Coefficient of determination
- ET:
-
Expression tree
- φ :
-
Helium porosity
- d PT :
-
Characteristic pore-throat diameter
- F :
-
Factor of formation resistivity
- k :
-
Permeability
- d g :
-
Mean grain size
- d d :
-
Dominant modal grain size
- m :
-
Cementation exponent
- B :
-
Sorting index
- η :
-
Multiplier
- IS:
-
Insertion sequence
- RIS:
-
Root insertion sequence
- f i :
-
Fitness
References
Al-Zainaldin S, Glover PWJ, Lorinczi P (2017) Synthetic fractal modeling of heterogeneous and anisotropic reservoirs for use in simulation studies: implications of their hydrocarbon recovery prediction. Transp Porous Med 116:181–212. https://doi.org/10.1007/s11242-016-0770-3
Al-Khalifah H, Glover PWJ, Lorinczi P (2020) Permeability prediction and diagenesis in tight carbonates using machine learning techniques. Mar Pet Geol 112:104096. https://doi.org/10.1016/j.marpetgeo.2019.104096
Azamathulla HMd, Ahmad Z (2012) Gene-expression programming for transverse mixing coefficient. J Hydrol 434–435:142–148. https://doi.org/10.1016/j.jhydrol.2012.02.018
Azamathulla HMd, Jarrett RD (2013) Use of gene expression programming to estimate Manning’s roughness coefficient for high gradient streams. Water Resour Manag 27:715–729. https://doi.org/10.1007/s11269-012-0211-1
Berg RR (1975) Capillary pressures in stratigraphic traps. AAPG Bull 59:939–956. https://doi.org/10.1306/83D91EF7-16C7-11D7-8645000102C1865D
Carman PC (1937) Fluid flow through granular beds. Trans Inst Chem Eng 15:150–166
Cui X, Bustin AMM, Bustin RM (2009) Measurements of gas permeability and diffusivity of tight reservoir rocks: different approaches and their applications. Geofluids 9:208–223. https://doi.org/10.1111/j.1468-8123.2009.00244.x
Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H (2015) Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). J Hydrol 529:1590–1600. https://doi.org/10.1016/j.jhydrol.2015.08.025
Faradonbeh RS, Armaghani DJ, Monjezi M, Mohamad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264. https://doi.org/10.1016/j.ijrmms.2016.07.028
Faradonbeh RS, Taheri A, SousaKarakus LREM (2020) Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches. Int J Rock Mech Min Sci 128:104279. https://doi.org/10.1016/j.ijrmms.2020.104279
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129
Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, Berlin
Glover PWJ, Zadjali II, Frew KA (2006) Permeability prediction from MICP and NMR data using an electrokinetic approach. Geophysics 71(4):F49–F60. https://doi.org/10.1190/1.2216930
Hagan MT, Demuth HB, Beale MH, De Jesus O (2014) Neural network design, 2nd edn. Martin Hagan Publisher, Stillwater
Hussein D, Collier R, Lawrence JA, Rashid F, Glover PWJ, Lorinczi P, Baban DH (2017) Stratigraphic correlation and paleoenvironmental analysis of the hydrocarbon-bearing Early Miocene Euphrates and Jeribe formations in the Zagros folded-thrust belt. Arab J Geosci 10:543. https://doi.org/10.1007/s12517-017-3342-0
Jafari S, Mahini SS (2017) Lightweight concrete design using gene expression programing. Constr Build Mater 139:93–100. https://doi.org/10.1016/j.conbuildmat.2017.01.120
Kozeny J. (1927) Über kapillare leitung des wassers im boden (aufstieg, versickerung und anwendung auf die bewässerung), Hölder-Pichler-Tempsky
Lis-Śledziona A (2019) Petrophysical rock typing and permeability prediction in tight sandstone reservoir. Acta Geophys 67:1895–1911. https://doi.org/10.1007/s11600-019-00348-5
Moghadam AA, Chalaturnyk R (2017) Rate dependency of permeability in tight rocks. J Nat Gas Sci Eng 40:208–225. https://doi.org/10.1016/j.jngse.2017.02.021
Murad Y, Ashteyat A, Hunaifat R (2019) Predictive model to the bond strength of FRP-to concrete under direct pullout using gene expression programming. J Civ Eng Manag 25(8):773–784. https://doi.org/10.3846/jcem.2019.10798
Nazari MH, Tavakoli V, Bonab HR, Yazdi MS (2019) Investigation of factors influencing geological heterogeneity in tight gas carbonates, Permian reservoir of the Persian Gulf. J Pet Sci Eng 183:106341. https://doi.org/10.1016/j.petrol.2019.106341
Onalo D, Adeligba S, Khan F, James LA, Butt S (2018) Data driven models for sonic well log prediction. J Pet Sci Eng 170:1022–1037. https://doi.org/10.1016/j.petrol.2018.06.072
Onalo D, Oloruntobi O, Adeligba S, Khan F, James L, Butt S (2019) Dynamic data drive sonic well log model for formation evaluation. J Pet Sci Eng 175:1049–1062. https://doi.org/10.1016/j.petrol.2019.01.042
Rashid F, Glover PWJ, Lorinczi P, Collier R, Lawrence J (2015a) Porosity and permeability of tight carbonate reservoir rocks in the north of Iraq. J Pet Sci Eng 133:147–161. https://doi.org/10.1016/j.petrol.2015.05.009
Rashid F, Glover PWJ, Lorinczi P, Hussein D, Collier R, Lawrence JA (2015b) Permeability prediction in tight carbonate rocks using capillary pressure measurements. Mar Pet Geol 68:536–550. https://doi.org/10.1016/j.marpetgeo.2015.10.005
Rashid F, Glover PWJ, Lorinczi P, Hussein D, Lawrence JA (2017) Microstructural controls on reservoir quality in tight oil carbonate reservoir rocks. J Pet Sci Eng 156:814–826. https://doi.org/10.1016/j.petrol.2017.06.056
Saridemir M (2010) Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Constr Build Mater 24(10):1911–1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011
Sen MK, Mallick S (2018) Generic algorithm with applications in geophysics. In: Application of soft computing and intelligent methods in geophysics. Springer Geophysics, Springer, Cham.
Sonebi M, Cevik A (2009) Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverized fuel ash. Constr Build Mater 23(7):2614–2622. https://doi.org/10.1016/j.conbuildmat.2009.02.012
Van Baaren JP (1979) Quick-look permeability estimates using sidewall samples and porosity logs. In: 6th Ann. European Logging Symp. Transactions, SPWLA, 19, London
Wang M, Wan W (2019) A new empirical formula for evaluating uniaxial compressive strength using the Schmidt hammer test. Int J Rock Mech Min Sci 123:104094. https://doi.org/10.1016/j.ijrmms.2019.104094
Acknowledgements
This work was supported by the National Key R & D Program of China (Grant no. 2017YFC1501000), the National Nature Science Foundation of China (Grant no. 42072303), and the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (Grant no. SKLGP2019Z007).
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Wei, Y., Xue, X. Permeability Prediction in Tight Carbonate Rocks Using Gene Expression Programming (GEP). Rock Mech Rock Eng 54, 2581–2593 (2021). https://doi.org/10.1007/s00603-021-02382-6
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DOI: https://doi.org/10.1007/s00603-021-02382-6