Skip to main content
SpringerLink
Log in

Predicting the efficiency of bare silica-based nano-fluid flooding in sandstone reservoirs for enhanced oil recovery through machine learning techniques using experimental data

  • Original Article
  • Published:
Applied Nanoscience Aims and scope Submit manuscript

Abstract

Determining additional oil recovery from a silica nano-fluid enhanced oil recovery project is essential prior to its field scale application to prepare economic feasibility of the project. Measurement of oil recovery is conventionally done through expensive and time-consuming laboratory methods. In pursuance of saving measurement’s cost and time, it is fundamental to develop fast and precise method, especially during the initial screening. This study uncovers the potential machine learning models, such as decision tree (DT) and extreme gradient boosting (XGBoost), as tools for predicting the expected oil recovery of silica-based nano-fluid flooding in sandstone reservoir. The application of mentioned tools for predicting the expected oil recovery has not been reported in the open literature. The models were developed using the results of 108 experiments. The model outputs were analyzed by performance matrices, such as root mean square error (RMSE), mean absolute error, mean square error, and co-efficient of determination (R2). The input features were size of nano-particles, concentration of nano-particles, oil viscosity, oil density, salinity of water, and porosity and permeability of the rock. The results showed that both DT and XGBoost models are reliable to predict the oil recovery with high R2 and low RMSE values of 0.9690 and 1.5837, and 0.9806 and 1.2608, for DT and XGBoost respectively. Heatmap analysis with Pearson's correlation co-efficient criterion showed that the porosity of rock, permeability of rock, concentration of nano-particles, and oil density have significantly more importance on the additional oil recovery.

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

Similar content being viewed by others

Abbreviations

ML:

Machine learning

DT:

Decision tree

XGBoost:

EXtreme gradient boosting

RMSE:

Root mean square error

MAE:

Mean absolute error

MSE:

Mean square error

R 2 :

Coefficient of determination

C :

Nano-particles concentration (wt%)

EOR:

Enhanced oil recovery

Wt:

Weight percentage

mD:

Mili Darcy

nm:

Nanometer

cp:

Centipoises

N :

Total number of observations

IFT:

Interfacial tension

References

  • Aadland R et al (2020) A core flood and microfluidics investigation of nanocellulose as a chemical additive to water flooding for EOR. Nanomaterials 10:1296

    Article  Google Scholar 

  • Adeniyi A, Ighalo JO, Marques G (2020) Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming. Int J Sustain Energ 40:1–16

    Google Scholar 

  • Ahmadi M et al (2019) Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid. J Therm Anal Calorim 139:2585–2599

    Article  Google Scholar 

  • Ahmed A (2018) Development of surface treated nanosilica for wettability alteration and interfacial tension reduction. J Dispers Sci Technol 39:1469–1475

    Article  Google Scholar 

  • Alomair O, Matar K, Alsaeed Y (2014) Nanofluids application for heavy oil recovery

  • Alsabaa A, et al (2020) Real-time prediction of rheological properties of all-oil mud using artificial intelligence

  • Balaji K, et al (2018) Status of data-driven methods and their applications in oil and gas industry. In: SPE Europec featured at 80th EAGE conference and exhibition

  • Ball K, et al (2017) The rise of the machines, analytics, and the digital oilfield: artificial intelligence in the age of machine learning and cognitive analytics. In: SPE/AAPG/SEG unconventional resources technology conference

  • Behzadi A, Mohammadi A (2016) Environmentally responsive surface-modified silica nanoparticles for enhanced oil recovery. J Nanoparticle Res 18:266

    Article  Google Scholar 

  • Bera A, Belhaj H (2016) Application of nanotechnology by means of nanoparticles and nanodispersions in oil recovery—a comprehensive review. J Nat Gas Sci Eng 34:1284–1309

    Article  Google Scholar 

  • Bila A, Torsæter O (2020) Enhancing oil recovery with hydrophilic polymer-coated silica nanoparticles. Energies 13:5720

    Article  Google Scholar 

  • Bila A, Torsæter O (2021) Experimental investigation of polymer-coated silica nanoparticles for EOR under harsh reservoir conditions of high temperature and salinity. Nanomaterials 11:765

    Article  Google Scholar 

  • Bondi G et al (2018) Using machine learning to predict soil bulk density on the basis of visual parameters: tools for in-field and post-field evaluation. Geoderma 318:137–147

    Article  Google Scholar 

  • Chandio TA et al (2021) Enhanced oil recovery by hydrophilic silica nanofluid: experimental evaluation of the impact of parameters and mechanisms on recovery potential. Energies 14(18):5767

    Article  Google Scholar 

  • Chen T-T, Lee S-J (2015) A weighted LS-SVM based learning system for time series forecasting. Inform Sci 299:99–116

    Article  Google Scholar 

  • Cheraghian G, Rostami S, Afrand M (2020) Nanotechnology in enhanced oil recovery. Processes 8:1073

    Article  Google Scholar 

  • Choi S et al (2017) Nanofluid enhanced oil recovery using hydrophobically associative zwitterionic polymer-coated silica nanoparticles. Energy Fuels 31(8):7777–7782

    Article  Google Scholar 

  • Choubey S, Karmakar GP (2021) Artificial intelligence techniques and their application in oil and gas industry. Artif Intell Rev 54:1–19

    Article  Google Scholar 

  • Ding Y, Meng X, Yang D (2018) Low salinity hot water injection with addition of nanoparticles for enhancing heavy oil recovery under reservoir conditions

  • El-Diasty A, Aly A (2015) Understanding the mechanism of nanoparticles applications in enhanced oil recovery

  • El-Diasty A, Salem A (2013) Applications of nanotechnology in the oil and gas industry: latest trends worldwide and future challenges in Egypt, vol 2

  • Elkatatny S et al (2018) Development of new mathematical model for compressional and shear sonic times from wireline log data using artificial intelligence neural networks (White Box). Arab J Sci Eng 43:6375–6389

    Article  Google Scholar 

  • Esfe M et al (2016) Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data. Int Commun Heat Mass Transf 75:192–196

    Article  Google Scholar 

  • Fako F, Shah S (2017) Emergence of nanotechnology in the oil and gas industry: emphasis on the application of silica nanoparticles. Petroleum 3:391–405

    Article  Google Scholar 

  • Hendraningrat L, Torsæter O (2015) A study of water chemistry extends the benefits of using silica-based nanoparticles on enhanced oil recovery. Appl Nanosci 6:83–95

    Article  Google Scholar 

  • Hendraningrat L, Li S, Torsæter O (2013a) A coreflood investigation of nanofluid enhanced oil recovery. J Petrol Sci Eng 11:128–138

    Article  Google Scholar 

  • Hendraningrat L, Li S, Torsæter O (2013b) Effect of some parameters influencing enhanced oil recovery process using silica nanoparticles: an experimental investigation

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice, 2nd edn. OTexts

  • Lavrakas P (2008) Encyclopedia of survey research methods. Sage, Thousand Oaks

    Book  Google Scholar 

  • Li Y et al (2017) Investigation of spontaneous imbibition by using a surfactant-free active silica water-based nanofluid for enhanced oil recovery. Energy Fuels 32:287–293

    Article  Google Scholar 

  • Maucec M, Garni S (2019) Application of automated machine learning for multi-variate prediction of well production. In: SPE middle east oil and gas show and conference

  • Miranda C, de Lara L, Tonetto B (2012) Stability and mobility of functionalized silica nanoparticles for enhanced oil recovery applications. Society of Petroleum Engineers—SPE International Oilfield Nanotechnology Conference

  • Negara A, et al. (2017) Unconfined compressive strength prediction from petrophysical properties and elemental spectroscopy using support-vector regression

  • Negi GS, Sircar A, Sivakumar P (2021) Applications of silica and titanium dioxide nanoparticles in enhanced oil recovery: promises and challenges. Petrol Res 6:224–246

    Article  Google Scholar 

  • Negin C, Saeedi A, Xie Q (2016) Application of nanotechnology for enhancing oil recovery: a review. Petroleum 2(4):324–333

    Article  Google Scholar 

  • Noshi CI, Schubert JJ (2018) The role of machine learning in drilling operations: a review. In: SPE/AAPG Eastern regional meeting

  • Peng B et al (2017) A review of nanomaterials for nanofluid enhanced oil recovery. RSC Adv 7:32246–32254

    Article  Google Scholar 

  • Ponnapati R et al (2011) Polymer-functionalized nanoparticles for improving waterflood sweep efficiency: characterization and transport properties. Ind Eng Chem Res 50:23

    Article  Google Scholar 

  • Raj I et al (2019) Ultralow concentration of molybdenum disulfide nanosheets for enhanced oil recovery. Fuel 251:514–522

    Article  Google Scholar 

  • Rellegadla S, Jain S, Agrawal A (2020) Application of nanoparticles-based technologies in the oil and gas industry. Springer, Berlin, pp 257–277

  • Rostami P et al (2019a) The effect of nanoparticles on wettability alteration for enhanced oil recovery: micromodel experimental studies and CFD simulation. Pet Sci 16:859–873

    Article  Google Scholar 

  • Rostami P, Aminshahidy B, Fahimpour J (2019b) Enhanced oil recovery using silica nanoparticles in the presence of salts for wettability alteration. J Dispers Sci Technol 41:402–413

    Article  Google Scholar 

  • Roustaei A, Saffarzadeh S, Mohammadi M (2013) An evaluation of modified silica nanoparticles’ efficiency in enhancing oil recovery of light and intermediate oil reservoirs. Egypt J Petrol 22:427–433

    Article  Google Scholar 

  • Sabah M et al (2019) Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: a case study from Marun oil field. J Petrol Sci Eng 177:236–249

    Article  Google Scholar 

  • Salem A (2015) A comparative investigation of nano particle effects for improved oil recovery: experimental work

  • Salem A, Hannora A (2015) An experimental investigation of silica nano particles for enhanced oil recovery applications

  • Shayan Nasr M et al (2021) Application of artificial intelligence to predict enhanced oil recovery using silica nanofluids. Nat Resour Res 30:2529–2542

    Article  Google Scholar 

  • Subasi A et al (2020) Permeability prediction of petroleum reservoirs using stochastic gradient boosting regression. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01986-0

    Article  Google Scholar 

  • Sun X (2017) Application of nanoparticles in enhanced oil recovery: a critical review of recent progress. Energies 10:345

    Article  Google Scholar 

  • Tang J et al (2017) An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Trans Intell Transp Syst 18:2340–2350

    Article  Google Scholar 

  • Tariq Z, Mahmoud M, Abdulraheem A (2020) An intelligent data-driven model for Dean–Stark water saturation prediction in carbonate rocks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04674-z

    Article  Google Scholar 

  • Wasan D, Nikolov A, Kondiparty K (2011) The wetting and spreading of nanofluids on solids: role of the structural disjoining pressure. Curr Opin Colloid Interface Sci 16:344–349

    Article  Google Scholar 

  • Wong K, Leon O (2010) Applications of nanofluids: current and future. Adv Mech Eng. https://doi.org/10.1155/2010/519659

    Article  Google Scholar 

  • Zallaghi M, Kharrat R, Hashemi A (2018) Improving the microscopic sweep efficiency of water flooding using silica nanoparticles. J Petrol Explor Prod Technol 8(1):259–269

    Article  Google Scholar 

  • Zargartalebi M, Kharrat R, Barati N (2015) Enhancement of surfactant flooding performance by the use of silica nanoparticles. Fuel 143:21–27

    Article  Google Scholar 

  • Zendehboudi S, Shafiei A, James L (2012) Nonionic surfactant for enhanced oil recovery from carbonates: adsorption kinetics and equilibrium. Ind Eng Chem Res 51:9894–9905

    Article  Google Scholar 

  • Zhang T et al (2013) Investigation of nanoparticle adsorption during transport in porous media. SPE J 20:667–677

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NED University of Engineering and Technology, Karachi, Pakistan

    Faizan Ali, Muhammad Arqam Khan, Ghulam Haider, Adnan ul-Haque & Ayesha Nadeem

  2. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Zeeshan Tariq

Authors
  1. Faizan Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Arqam Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghulam Haider
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adnan ul-Haque
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zeeshan Tariq
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ayesha Nadeem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Arqam Khan.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

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

Ali, F., Khan, M.A., Haider, G. et al. Predicting the efficiency of bare silica-based nano-fluid flooding in sandstone reservoirs for enhanced oil recovery through machine learning techniques using experimental data. Appl Nanosci 12, 2367–2377 (2022). https://doi.org/10.1007/s13204-022-02529-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13204-022-02529-z

Keywords

Search

Navigation