Skip to main content

Advertisement

SpringerLink
Log in

A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of various digenetic properties. Consequently, the permeability calculation of tight rocks is costly, and only a finite number of core plugs in any single reservoir can be estimated. Hence, in the present study, a novel hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), i.e., IHHO, and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables. The proposed IHHO employs a mutation mechanism to avoid trapping in local optima by increasing the search capabilities. Subsequently, ELM-IHHO, a novel metaheuristic ELM-based algorithm, was developed to predict the permeability of tight carbonates. Experimental results show that the proposed ELM-IHHO attained the most accurate prediction with R2 = 0.9254 and RMSE = 0.0619 in the testing phase. The result of the proposed model is significantly better than those obtained from other ELM-based hybrid models developed with particle swarm optimisation, genetic algorithm, and slime mould algorithm. The results also illustrate that the proposed ELM-IHHO model outperforms the other benchmark model, such as back-propagation neural nets, support vector regression, random forest, and group method of data handling in predicting the permeability of tight carbonates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Ma YZ, Holditch S (2015) Unconventional oil and gas resources handbook: evaluation and development. Gulf Professional Publishing, Waltham

    Google Scholar 

  2. Hussein D, Collier R, Lawrence J, Rashid F, Glover P, Lorinczi P et al (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(24):543

    Article  Google Scholar 

  3. Rashid F, Glover P, Lorinczi P, Hussein D, Lawrence J (2017) Microstructural controls on reservoir quality in tight oil carbonate reservoir rocks. J Pet Sci Eng 156:814–826

    Article  Google Scholar 

  4. Zhang X, Spiers CJ, Peach CJ, Hebing A, Geoconsultants P (2013) Tight rock permeability measurement by pressure pulse decay and modeling. In: Proceedings of the international symposium of the Society of Core Analysts, Napa Valley, California, USA. 2013

  5. Akai T, Takakuwa Y, Sato K, Wood J (2016) Pressure dependent permeability of tight rocks. In: SPE low perm symposium. Society of Petroleum Engineers; 2016

  6. Al-Zainaldin S, Glover PW, Lorinczi P (2017) Synthetic fractal modelling of heterogeneous and anisotropic reservoirs for use in simulation studies: implications on their hydrocarbon recovery prediction. Transp Porous Media 116(1):181–212

    Article  Google Scholar 

  7. Glover PW, Lorinczi P, Al-Zainaldin S, Al-Ramadan H, Daniel G, Sinan S (2018). Advanced fractal modelling of heterogeneous and anisotropic reservoirs. In: SPWLA 59th annual logging symposium. Society of Petrophysicists and Well-Log Analysts; 2018

  8. Guo H, Zhou J, Koopialipoor M, Armaghani DJ, Tahir M (2019) Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput 37(1):1–14

    Google Scholar 

  9. Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https://doi.org/10.1007/s00366-020-01003-0

    Article  Google Scholar 

  10. Dhiman G (2019) ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Eng Comput. https://doi.org/10.1007/s00366-019-00826-w

    Article  Google Scholar 

  11. Maleki E, Unal O (2020) Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN. Eng Comput. https://doi.org/10.1007/s00366-020-00964-6

    Article  Google Scholar 

  12. Zhu L, Zhang C, Zhang C, Wei Y, Zhou X, Cheng Y et al (2018) Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves. J Geophys Eng 15(3):1050–1061

    Article  Google Scholar 

  13. Zhu L, Zhang C, Zhang C, Zhang Z, Nie X, Zhou X et al (2019) Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning. Appl Soft Comput 83:105596

    Article  Google Scholar 

  14. Onalo D, Adedigba S, Khan F, James LA, Butt S (2018) Data driven model for sonic well log prediction. J Pet Sci Eng 170:1022–1037

    Article  Google Scholar 

  15. Onalo D, Oloruntobi O, Adedigba S, Khan F, James L, Butt S (2019) Dynamic data driven sonic well log model for formation evaluation. J Pet Sci Eng 175:1049–1062

    Article  Google Scholar 

  16. Zhu L, Zhang C, Zhang C, Zhang Z, Zhou X, Zhu B (2019) An improved theoretical nonelectric water saturation method for organic shale reservoirs. IEEE Access 7:51441–51456

    Article  Google Scholar 

  17. Xue Y, Cheng L, Mou J, Zhao W (2014) A new fracture prediction method by combining genetic algorithm with neural network in low-permeability reservoirs. J Pet Sci Eng 121:159–166

    Article  Google Scholar 

  18. Wang H, Wu W, Chen T, Dong X, Wang G (2019) An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. J Pet Sci Eng 176:664–678

    Article  Google Scholar 

  19. Lim J-S, Kim J (2004) Reservoir porosity and permeability estimation from well logs using fuzzy logic and neural networks. In: SPE Asia Pacific oil and gas conference and exhibition. Society of Petroleum Engineers; 2004

  20. Tang H (2008) Improved carbonate reservoir facies classification using artificial neural network method. In: Canadian international petroleum conference. Petroleum Society of Canada; 2008

  21. Tang H, Meddaugh WS, Toomey N (2011) Using an artificial-neural-network method to predict carbonate well log facies successfully. SPE Reserv Eval Eng 14(01):35–44

    Article  Google Scholar 

  22. Zhou X, Zhang C, Zhang Z, Zhang R, Zhu L, Zhang C (2019) A saturation evaluation method in tight gas sandstones based on diagenetic facies. Mar Pet Geol 107:310–325

    Article  Google Scholar 

  23. Zhu L, Zhang C, Wei Y, Zhou X, Huang Y, Zhang C (2017) Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data. Interpretation 5(3):T341–T350

    Article  Google Scholar 

  24. 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. Arab J Sci Eng 42(4):1643–1654

    Article  Google Scholar 

  25. Rashid F, Glover P, Lorinczi P, Hussein D, Collier R, Lawrence J (2015) Permeability prediction in tight carbonate rocks using capillary pressure measurements. Mar Pet Geol 68:536–550

    Article  Google Scholar 

  26. Beşdok E (2004) A new method for impulsive noise suppression from highly distorted images by using Anfis. Eng Appl Artif Intell 17(5):519–527

    Article  Google Scholar 

  27. Huang J-W, Chiang C-W, Chang J-W (2018) Email security level classification of imbalanced data using artificial neural network: the real case in a world-leading enterprise. Eng Appl Artif Intell 75:11–21

    Article  Google Scholar 

  28. Janakiraman VM, Nguyen X, Assanis D (2016) An ELM based predictive control method for HCCI engines. Eng Appl Artif Intell 48:106–118

    Article  Google Scholar 

  29. Shahraiyni HT, Sodoudi S, Kerschbaumer A, Cubasch U (2015) A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas. Eng Appl Artif Intell 41:175–182

    Article  Google Scholar 

  30. Singh V, Gupta I, Gupta H (2007) ANN-based estimator for distillation using Levenberg–Marquardt approach. Eng Appl Artif Intell 20(2):249–259

    Article  Google Scholar 

  31. Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K (2021) Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 145:106449

    Article  Google Scholar 

  32. Asteris PG, Koopialipoor M, Armaghani DJ, Kotsonis EA, Lourenço PB (2021) Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06004-8

    Article  Google Scholar 

  33. Zhou J, Huang S, Wang M, Qiu Y (2021) Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Eng Comput. https://doi.org/10.1007/s00366-021-01418-3

    Article  Google Scholar 

  34. Zeng J, Roy B, Kumar D, Mohammed AS, Armaghani DJ, Zhou J et al (2021) Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Eng Comput. https://doi.org/10.1007/s00366-020-01225-2

    Article  Google Scholar 

  35. Xie C, Nguyen H, Bui X-N, Choi Y, Zhou J, Nguyen-Trang T (2021) Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms. Geosci Front 12(3):101108

    Article  Google Scholar 

  36. Kardani N, Bardhan A, Samui P, Nazem M, Zhou A, Armaghani DJ (2021) A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil. Eng Comput 1–20

  37. Kardani N, Zhou A, Shen S-L, Nazem M (2021) Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches. Transp Geotech. https://doi.org/10.1016/j.trgeo.2021.100591

    Article  Google Scholar 

  38. R Kaloop M, Bardhan A, Kardani N, Samui P, Hu JW, Ramzy A (2021) Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power. Renew Sustain Energy Rev 148:111315. https://doi.org/10.1016/j.rser.2021.111315

  39. Bardhan A, Samui P, Ghosh K, H. Gandomi A, Bhattacharyya S (2021) ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2021.107595

    Article  Google Scholar 

  40. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  41. Hecht-Nielsen R (1992) Theory of the backpropagation neural network. Neural networks for perception. Elsevier, Amsterdam, pp 65–93

    Book  Google Scholar 

  42. Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242

    Article  Google Scholar 

  43. Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V (1996) Support vector regression machines. Adv Neural Inf Process Syst 9:155–161

    Google Scholar 

  44. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition. 1. IEEE, pp 278–82

  45. Ivakhnenko A, Ivakhnenko G (1995) The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern Recognit Image Anal 5:527–535

    Google Scholar 

  46. Kardani N, Zhou A, Nazem M, Shen S-L (2021) Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J Rock Mech Geotechn Eng 13(1):188–201

    Article  Google Scholar 

  47. Kardani N, Zhou A, Nazem M, Lin X (2021) Modelling of municipal solid waste gasification using an optimised ensemble soft computing model. Fuel 289:119903

    Article  Google Scholar 

  48. Kardani N, Bardhan A, Kim D, Samui P, Zhou A (2021) Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO. J Build Eng 35:102105

  49. Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 35(3):419–427

    Article  MATH  Google Scholar 

  50. Samui P, Kumar B (2006) Artificial neural network prediction of stability numbers for two-layered slopes with associated flow rule. Electron J Geotech Eng 11:1–44

    Google Scholar 

  51. Kardani MN, Baghban A, Hamzehie ME, Baghban M (2019) Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach. Pet Sci Technol 37(16):1861–1867

    Article  Google Scholar 

  52. Kardani MN, Baghban A (2017) Utilization of LSSVM strategy to predict water content of sweet natural gas. Pet Sci Technol 35(8):761–767

    Article  Google Scholar 

  53. Shang Z, He J (2015) Confidence-weighted extreme learning machine for regression problems. Neurocomputing 148:544–550

    Article  Google Scholar 

  54. Zhou J, Qiu Y, Armaghani DJ, Zhang W, Li C, Zhu S, Tarinejad R (2021) Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geosci Front 12(3):101091. https://doi.org/10.1016/j.gsf.2020.09.020

    Article  Google Scholar 

  55. Zhou J, Qiu Y, Zhu S, Armaghani DJ, Li C, Nguyen H, Yagiz S (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intel 97:104015

    Article  Google Scholar 

  56. Qiu Y, Zhou J, Khandelwal M, Yang H, Yang P, Li C (2021) Performance evaluation of hybrid WOA-XGBoost, GWOXGBoost and BO-XGBoost models to predict blast-induced ground vibration. Eng Comput. https://doi.org/10.1007/s00366-021-01393-9

    Article  Google Scholar 

  57. Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529

    Article  Google Scholar 

  58. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  59. Wolpert DH, Macready WG (1995) No free lunch theorems for search. Technical Report SFI-TR-95-02-010, Santa Fe Institute

  60. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks. IEEE, vol 4, pp 1942–1948

  61. Kardani MN, Baghban A, Sasanipour J, Mohammadi AH, Habibzadeh S (2018) Group contribution methods for estimating CO2 absorption capacities of imidazolium and ammonium-based polyionic liquids. J Clean Prod 203:601–618

    Article  Google Scholar 

  62. Kardani N, Zhou A, Nazem M, Shen S-L (2020) Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotech Geol Eng 38(2):2271–2291

    Article  Google Scholar 

  63. Holland JH (1992) Genetic algorithms. Sci Am 267(1):44–50

    Article  Google Scholar 

  64. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  65. Koopialipoor M, Fallah A, Armaghani DJ, Azizi A, Mohamad ET (2019) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 35(1):243–256

    Article  Google Scholar 

  66. Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9(13):2630

    Article  Google Scholar 

  67. Roy B, Singh MP, Singh A (2019) A novel approach for rainfall-runoff modelling using a biogeography-based optimization technique. Int J River Basin Manag 19(1):1–14

    Article  Google Scholar 

  68. Cai R, Han T, Liao W, Huang J, Li D, Kumar A et al (2020) Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cem Concr Res 136:106164

    Article  Google Scholar 

  69. Golafshani EM, Behnood A, Arashpour M (2020) Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer. Constr Build Mater 232:117266

    Article  Google Scholar 

  70. Al Khalifah H, Glover P, Lorinczi P (2020) Permeability prediction and diagenesis in tight carbonates using machine learning techniques. Mar Pet Geol 112:104096

    Article  Google Scholar 

  71. Barton C, Woods M, Bristow C, Newall A, Westhead R, Evans DJ et al (2011) Geology of south Dorset and south-east Devon and its World Heritage Coast: special memoir for 1: 50,000 geological sheets 328 Dorchester, 341/342 West Fleet and Weymouth and 342/343 Swanage, and parts of sheets 326/340 Sidmouth, 327 Bridport, 329 Bournemouth and 339 Newton Abbot. British Geological Survey

  72. Asteris PG, Mamou A, Hajihassani M, Hasanipanah M, Koopialipoor M, Le T-T et al (2021) Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp Geotech. https://doi.org/10.1016/j.trgeo.2021.100588

    Article  Google Scholar 

  73. Kumar M, Bardhan A, Samui P, Hu JW, Kaloop RM (2021) Reliability analysis of pile foundation using soft computing techniques: a comparative study. Processes 9(3):486

    Article  Google Scholar 

  74. Ghanbari A, Kardani MN, Moazami Goodarzi A, Janghorban Lariche M, Baghban A (2020) Neural computing approach for estimation of natural gas dew point temperature in glycol dehydration plant. Int J Ambient Energy 41(7):775–782

    Article  Google Scholar 

  75. Ghani SK, Bardhan A (2021) A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models. Sādhanā 46(3)

  76. Bi J, Bennett KP (2003) Regression error characteristic curves. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 43–50

  77. Kozeny J (1927) Uber kapillare leitung der wasser in boden. R Acad Sci Vienna Proc Class I 136:271–306

    Google Scholar 

  78. Carman PC (1937) Fluid flow through granular beds. Trans Inst Chem Eng 15:150–166

    Google Scholar 

  79. Berg RR (1975) Capillary pressures in stratigraphic traps. AAPG Bull 59(6):939–956

    Google Scholar 

  80. Van Baaren J (1979) Quick-look permeability estimates using sidewall samples and porosity logs. In: Trans. 6th Annual European logging symposium, Society of Professional Well Log Analysts

  81. Glover P, Zadjali I, Frew K (2006) Permeability prediction from MICP and NMR data using an electrokinetic approach. Geophysics 71(4):F49–F60

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil and Infrastructure Engineering Discipline, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC, 3001, Australia

    Navid Kardani, Majidreza Nazem & Annan Zhou

  2. Department of Civil Engineering, National Institute of Technology (NIT) Patna, Patna, Bihar, 800005, India

    Abidhan Bardhan & Pijush Samui

  3. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, MP, 466114, India

    Bishwajit Roy

  4. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, 454080, Russian Federation

    Danial Jahed Armaghani

Authors
  1. Navid Kardani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abidhan Bardhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bishwajit Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pijush Samui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Majidreza Nazem
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Annan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NK: main author, conceptualisation, development of hybrid and AI models, statistical analysis, detailing, overall analysis, visualisation, original draft and manuscript finalisation; AB: development of AI and hybrid models and statistical analysis, detailing, overall analysis; BR: development of hybrid models; PS: overall review; MN: detailed review and editing; DJA: detailed review and editing; AZ: detailed review and editing.

Corresponding authors

Correspondence to Navid Kardani or Annan Zhou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kardani, N., Bardhan, A., Roy, B. et al. A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates. Engineering with Computers 38 (Suppl 5), 4323–4346 (2022). https://doi.org/10.1007/s00366-021-01466-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01466-9

Keywords

Search

Navigation