Skip to main content
SpringerLink
Log in

Real-time static Poisson’s ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

The experimental Poisson’s ratio prediction is time-consuming and expensive and resulted in discontinuous profile. Besides, the limited applicability of the existing empirical correlations highlights the application of artificial intelligence with its booming utilization in petroleum industry. The purpose of this work is to develop several artificial intelligence models for predicting real-time static Poisson’s ratio of complex lithology while drilling. The artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) techniques were utilized using the drilling parameters as inputs. Data points (1775) from a vertical well, containing sand, shale, and carbonate lithologies, were used to develop the models. The models were validated using different dataset from another well. New empirical correlation was extracted based on the optimized ANN approach. The three developed models predicted the static Poisson’s ratio at good matching accuracy. The correlation coefficient (R) and average absolute percentage error (AAPE) of the developed models range from 0.95 to 0.96 and 2.18 to 5.79% for training process, respectively, while in testing process, the R and AAPE values range from 0.92 to 0.93 and 5.81 to 6.74%. The validation process confirmed the reliability of the developed models with R values of 0.90, 0.91, and 0.90 and AAPE of 6.57, 7.25, and 8.12% for SVM, ANFIS, and ANN approaches, respectively. The developed ANN-based model was switched into a white box model with new empirical correlation, which is applicable with the extracted weights and biases. The constructed models can predict inexpensively the static Poisson’s ratio for multiple lithologies in real-time at reasonable accuracy.

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

Similar content being viewed by others

Abbreviations

AAPE:

average absolute percentage error

ACE:

alternating conditional expectation

AI:

artificial intelligence

ANFIS:

adaptive neuro-fuzzy inference system

ANN:

artificial neural network

DE:

differential evolution algorithm

FL:

fuzzy logic

FN:

functional networks

newff:

feed forward neural network function

ROP:

rate of penetration, ft/h

RPM:

rotation speed, rotation per minute

SPP:

standpipe pressure, psi

SVM:

support vector machines

T:

torque, klbf.ft

tansig:

hyperbolic tangent sigmoid transfer function

trainbr:

Bayesian regularization backpropagation training function

WOB:

weight on bit, klbm

a, b, c, and d :

different empirical constants

b i :

biases of input layer

i :

neuron index

NN :

number of neurons

Q :

pumping rate, gal/minute

R :

correlation coefficient

V P :

compressional-wave velocity, km/s

V S :

shear-wave velocity, km/s

V Sh :

shale volume factor

v dyn :

dynamic Poisson’s ratio

v st :

static Poisson’s ratio

w 1 :

weights between input and hidden layers

w 2 :

weights between hidden and output layers

References

  • Abdelgawad K, Elkatatny S, Mousa T, et al (2018) Real time determination of rheological properties of spud drilling fluids using a hybrid artificial intelligence technique. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April. Society of Petroleum Engineers, Dammam, Saudi Arabia, p 13

  • Abdelgawad KZ, Elzenary M, Elkatatny S, Mahmoud M, Abdulraheem A, Patil S (2019) New approach to evaluate the equivalent circulating density ( ECD ) using artificial intelligence techniques. J Pet Explor Prod Technol 9:1569–1578. https://doi.org/10.1007/s13202-018-0572-y

    Article  Google Scholar 

  • Abdulraheem A (2019) Prediction of Poisson’s ratio for carbonate rocks using ann and fuzzy logic type-2 approaches. In: International Petroleum Technology Conference, Beijing, China, 26-28 March. International Petroleum Technology Conference

  • Abdulraheem A, Ahmed M, Vantala A, Parvez T (2009) Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. SPE:126094

  • Agwu OE, Akpabio JU, Alabi SB, Dosunmu A (2018) Artificial intelligence techniques and their applications in drilling fluid engineering: a review. J Pet Sci Eng 167:300–315. https://doi.org/10.1016/j.petrol.2018.04.019

    Article  Google Scholar 

  • Ahmadi MA, Pournik M, Shadizadeh SR (2015a) Toward connectionist model for predicting bubble point pressure of crude oils: application of artificial intelligence. Petroleum 1:307–317. https://doi.org/10.1016/j.petlm.2015.08.003

    Article  Google Scholar 

  • Ahmadi MA, Soleimani R, Lee M, Kashiwao T, Bahadori A (2015b) Determination of oil well production performance using arti fi cial neural network ( ANN ) linked to the particle swarm optimization ( PSO ) tool. Petroleum 1:118–132. https://doi.org/10.1016/j.petlm.2015.06.004

    Article  Google Scholar 

  • Ahmed A, Ali A, Elkatatny S, Abdulraheem A (2019) New artificial neural networks model for predicting rate of penetration in deep shale formation. Sustainability 11:6527. https://doi.org/10.3390/su11226527

    Article  Google Scholar 

  • Al-AbdulJabbar A, Elkatatny S, Mahmoud M, Abdulraheem A (2018) Predicting rate of penetration using artificial intelligence techniques. in: spe kingdom of saudi arabia annual technical symposium and exhibition, Dammam, Saudi Arabia, 23-26 April. Society of Petroleum Engineers, Dammam, p 10

  • Al-abduljabbar A, Al-azani K, Elkatatny S (2020a) Estimation of reservoir porosity from drilling parameters using artificial neural networks. Petrophysics:61, 318–330. https://doi.org/10.30632/PJV61N3-2020a5

  • Al-abduljabbar A, Elkatatny S, Mahmoud AA et al (2020b) Prediction of the rate of penetration while drilling horizontal carbonate reservoirs using the self-adaptive artificial neural networks technique. Sustainability 12:1376. https://doi.org/10.3390/su12041376

    Article  Google Scholar 

  • Alakbari FS, Elkatatny S, Baarimah SO (2016) Prediction of bubble point pressure using artificial intelligence AI techniques. In: SPE Middle East Artificial Lift Conference and Exhibition, Manama, Kingdom of Bahrain, 30 November-1 December. Society of Petroleum Engineers, Manama, p 9

  • Al-anazi BD, Algarni MT, Tale M, Almushiqeh I (2011) Prediction of Poisson’s ratio and Young’s modulus for hydrocarbon reservoirs using alternating conditional expectation algorithm. In: SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 25-28 September. Society of Petroleum Engineers, Manama, p 9

  • Al-Azani K, Elkatatny S, Abdulraheem A, et al (2018) Real time prediction of the rheological properties of oil-based drilling fluids using artificial neural networks. SPE Kingdom Saudi Arab. Annu. Tech. Symp. Exhib. Dammam, Saudi Arab. 23-26 April 17

  • Ali A, Aïfa T, Baddari K (2014) Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an arti fi cial neural network in Hassi Messaoud oil field, Algeria. J Pet Sci Eng 115:78–89. https://doi.org/10.1016/j.petrol.2014.01.011

    Article  Google Scholar 

  • Anifowose F, Labadin J, Abdulraheem A (2015) Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. Appl Soft Comput 26:483–496. https://doi.org/10.1016/j.asoc.2014.10.017

    Article  Google Scholar 

  • Bello O, Holzmann J, Yaqoob T, Teodoriu C (2015) Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J Artif Intell Soft Comput Res 5:121–139. https://doi.org/10.1515/jaiscr-2015-0024

    Article  Google Scholar 

  • Bourgoyne ATJ, Millheim KK, Chenevert ME, Young, F.S. J (1986) Applied drilling engineering, Volume 2. Society of Petroleum Engineers, Houston

  • Christaras B, Auger F, Mosse E (1994) Determination of the moduli of elasticity of rocks. Comparison of the ultrasonic velocity and mechanical resonance frequency methods with direct static methods. Mater Struct 27:222–228. https://doi.org/10.1007/BF02473036

    Article  Google Scholar 

  • Elkatatny SM (2016) Determination the rheological properties of invert emulsion based mud on real time using artificial neural network. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 25-28 April. Society of Petroleum Engineers, Dammam, p 13

  • Elkatatny S (2018) New approach to optimize the rate of penetration using artificial neural network. Arab J Sci Eng 43:6297–6304. https://doi.org/10.1007/s13369-017-3022-0

    Article  Google Scholar 

  • Elkatatny (2019) Real-time prediction of the rheological properties of water-based drill-in fluid using artificial neural networks. Sustainability 11:5008. https://doi.org/10.3390/su11185008

    Article  Google Scholar 

  • Elkatatny S (2020) Real-time prediction of rate of penetration in s-shape well profile using artificial intelligence models. Sensors 20:3506. https://doi.org/10.3390/s20123506

    Article  Google Scholar 

  • Elkatatny S, Mahmoud M (2017) Real time prediction of the rheological parameters of NaCl water-based drilling fluid using artificial neural networks. SPE Kingdom Saudi Arab. Annu. Tech. Symp. Exhib. Dammam, Saudi Arab. 24-27 April 15

  • Elkatatny S, Mahmoud M (2018a) Development of a new correlation for bubble point pressure in oil reservoirs using artificial intelligent technique. Arab J Sci Eng 43:2491–2500. https://doi.org/10.1007/s13369-017-2589-9

    Article  Google Scholar 

  • Elkatatny S, Mahmoud M (2018b) Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique. Petroleum 4:178–186. https://doi.org/10.1016/j.petlm.2017.09.009

    Article  Google Scholar 

  • Elkatatny S, Tariq Z, Mahmoud M (2016a) Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). J Pet Sci Eng 146:1202–1210. https://doi.org/10.1016/j.petrol.2016.08.021

    Article  Google Scholar 

  • Elkatatny SM, Zeeshan T, Mahmoud M, et al (2016b) Application of artificial intelligent techniques to determine sonic time from well logs. In: 50th U.S. Rock Mechanics/Geomechanics Symposium, Houston, Texas, 26-29 June. American Rock Mechanics Association, p 11

  • Elkatatny SM, Tariq Z, Mahmoud MA, et al (2017a) An artificial intelligent approach to predict static Poisson’s ratio. In: 51st U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, 25-28 June. American Rock Mechanics Association, San Francisco, p 7

  • Elkatatny SM, Tariq Z, Mahmoud MA, Al-AbdulJabbar A (2017b) Optimization of rate of penetration using artificial intelligent techniques. In: 51st U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, 25-28 June. American Rock Mechanics Association, p 8

  • Elkatatny S, Mahmoud M, Tariq Z, Abdulraheem A (2018a) New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network. Neural Comput & Applic 30:2673–2683. https://doi.org/10.1007/s00521-017-2850-x

    Article  Google Scholar 

  • Elkatatny S, Moussa T, Abdulraheem A, Mahmoud M (2018b) A self-adaptive artificial intelligence technique to predict oil pressure volume temperature properties. Energies 11:3490. https://doi.org/10.3390/en11123490

    Article  Google Scholar 

  • Elkatatny S, Tariq Z, Mahmoud M, Mohamed I, Abdulraheem A (2018c) 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. https://doi.org/10.1007/s13369-018-3094-5

    Article  Google Scholar 

  • Elkatatny S, Tariq Z, Mahmoud M, Abdulraheem A (2018d) New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs. Petroleum 4:408–418. https://doi.org/10.1016/j.petlm.2018.04.002

    Article  Google Scholar 

  • Elzenary M, Elkatatny S, Abdelgawad KZ, et al (2018) New technology to evaluate equivalent circulating density while drilling using artificial intelligence. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26. Society of Petroleum Engineers, Dammam, Saudi Arabia, p 14

  • Feng C, Wang Z, Deng X, Fu J, Shi Y, Zhang H, Mao Z (2019) A new empirical method based on piecewise linear model to predict static Poisson’s ratio via well logs. J Pet Sci Eng 175:1–8. https://doi.org/10.1016/j.petrol.2018.11.062

    Article  Google Scholar 

  • Fjar E, Holt RM, Raaen AM, Horsrud P (2008) Petroleum related rock mechanics, Volume 53. Elsevier Science

  • Gomaa I, Elkatatny S, Abdulraheem A (2020) Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network. J Nat Gas Sci Eng 77:103224. https://doi.org/10.1016/j.jngse.2020.103224

    Article  Google Scholar 

  • González JW, Valdez R, Torres J, Medina F (2018) Identification of zones of abnormal pressures and determination of the mechanical properties of the rock through pseudo-sonic and pseudo-density logs in conventional and unconventional reservoirs. In: SPE Argentina Exploration and Production of Unconventional Resources Symposium, Neuquén, Argentina, 14-16 August. Society of Petroleum Engineers

  • Gowida A, Elkatatny S (2020) Prediction of sonic wave transit times from drilling parameters while horizontal drilling in carbonate rocks using neural networks. Petrophysics 61:482–494. https://doi.org/10.30632/PJV61N5-2020a6

    Article  Google Scholar 

  • Gowida A, Elkatatny S, Abdulraheem A (2019a) Application of artificial neural network to predict formation bulk density while drilling. Petrophysics 60:660–674. https://doi.org/10.30632/PJV60N5-2019a9

    Article  Google Scholar 

  • Gowida A, Elkatatny S, Ramadan E, Abdulraheem A (2019b) Data-driven framework to predict the rheological properties of CaCl2 brine-based drill-in fluid using artificial neural network. Energies 12:1880. https://doi.org/10.3390/en12101880

    Article  Google Scholar 

  • Gowida A, Elkatatny S, Abdelgawad K, Gajbhiye R (2020a) Newly developed correlations to predict the rheological parameters of high-bentonite drilling fluid using neural networks. Sensors 20:2787. https://doi.org/10.3390/s20102787

    Article  Google Scholar 

  • Gowida A, Elkatatny S, Al-afnan S, Abdulraheem A (2020b) New computational artificial intelligence models for generating synthetic formation bulk density logs while drilling. Sustainability 12:686. https://doi.org/10.3390/su12020686

    Article  Google Scholar 

  • Guo Y, Hansen RO, Harthill N (1992) Feature recognition from potential fields using neural networks. In: SEG Technical Program Expanded Abstracts 1992. Society of Exploration Geophysicists 1–5

  • Hammah R, Curran J, Yacoub T (2006) The influence of Young’s modulus on stress modelling results. In: Golden Rocks 2006, The 41st U.S. Symposium on Rock Mechanics (USRMS), 17-21 June, Golden, Colorado. American Rock Mechanics Association, p 5

  • Hassan A, Al-Majed A, Mahmoud M, et al (2019a) Improved predictions in oil operations using artificial intelligent techniques. In: SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 18-21 March. Society of Petroleum Engineers

  • Hassan A, Elkatatny S, Abdulraheem A (2019b) Application of artificial intelligence techniques to predict the well productivity of fishbone wells. Sustainability 11:6083. https://doi.org/10.3390/su11216083

    Article  Google Scholar 

  • Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  Google Scholar 

  • Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  • Khalifah H Al, 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

  • Kononov A, Gisolf D, Verschuur E (2007) Application of neural networks to traveltime computation. In: SEG Technical Program Expanded Abstracts 2007. Society of Exploration Geophysicists:1785–1789

  • Kumar J (1976) The effect of Poisson’s ratio on rock properties. In: SPE Annual Fall Technical Conference and Exhibition, New Orleans, Louisiana, 3-6 October. Society of Petroleum Engineers, New Orleans, Louisiana, p 12

  • Labudovic V (1984) The effect of Poisson’s ratio on fracture height. J Pet Technol 36:287–290. https://doi.org/10.2118/10307-PA

    Article  Google Scholar 

  • Lippmann R (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22. https://doi.org/10.1109/MASSP.1987.1165576

    Article  Google Scholar 

  • Mahdiani MR, Norouzi M (2018) A new heuristic model for estimating the oil formation volume factor. Petroleum 4:300–308. https://doi.org/10.1016/j.petlm.2018.03.006

    Article  Google Scholar 

  • Mensa-Wilmot G, Calhoun B, Perrin VP (1999) Formation drillability-definition, quantification and contributions to bit performance evaluation. In: SPE/IADC Middle East Drilling Technology Conference, Abu Dhabi, UAE, 8-10 November. Society of Petroleum Engineers

  • Moussa T, Elkatatny S, Mahmoud M, Abdulraheem A (2018) Development of new permeability formulation from well log data using artificial intelligence approaches. J Energy Resour Technol 140:072903. https://doi.org/10.1115/1.4039270

    Article  Google Scholar 

  • Nakamoto P (2017) Neural networks and deep learning: deep learning explained to your granny a visual introduction for beginners who want to make their own deep learning neural network (machine learning). CreateSpace Independent Publishing Platform, USA

  • Nes O-M, Fjær E, Tronvoll J, et al (2005) Drilling time reduction through an integrated rock mechanics analysis. In: SPE/IADC Drilling Conference, Amsterdam, Netherlands, 23-25 February. Society of Petroleum Engineers, Amsterdam, Netherlands, p 7

  • Niculescu SP (2003) Artificial neural networks and genetic algorithms in QSAR. J Mol Struct THEOCHEM 622:71–83. https://doi.org/10.1016/S0166-1280(02)00619-X

    Article  Google Scholar 

  • Oloso MA, Hassan MG, Bader-El-Den MB, Buick JM (2017) Hybrid functional networks for oil reservoir PVT characterisation. Expert Syst Appl 87:363–369. https://doi.org/10.1016/j.eswa.2017.06.014

    Article  Google Scholar 

  • Popa AS, Cassidy SD (2012) Artificial intelligence for heavy oil assets: the evolution of solutions and organization capability. In: SPE Annual Technical Conference and Exhibition, , San Antonio, Texas, 8-10 October. Society of Petroleum Engineers

  • Rao SS, Ramamurti V (1993) A hybrid technique to enhance the performance of recurrent neural networks for time series prediction. In: IEEE International Conference on Neural Networks, 28 March-1 April. USA. IEEE, San Francisco, pp 52–57

    Chapter  Google Scholar 

  • Ross C (2017) Improving resolution and clarity with neural networks. In: SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists 3072–3076

  • Shokooh Saljooghi B, Hezarkhani A (2015) A new approach to improve permeability prediction of petroleum reservoirs using neural network adaptive wavelet (wavenet). J Pet Sci Eng 133:851–861. https://doi.org/10.1016/j.petrol.2015.04.002

    Article  Google Scholar 

  • Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18–27. https://doi.org/10.1016/j.cageo.2012.02.004

    Article  Google Scholar 

  • Tariq Z, Elkatatny S, Mahmoud M, Abdulraheem A (2016) a new artificial intelligence based empirical correlation to predict sonic travel time. In: International Petroleum Technology Conference. International Petroleum Technology Conference, Bangkok, Thailand, p 19

  • Tariq Z, Elkatatny S, Mahmoud M, et al (2017) A new technique to develop rock strength correlation using artificial intelligence tools. In: SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, 8–10 May. Society of Petroleum Engineers, Abu Dhabi, UAE, p 14

  • Tariq Z, Abdulraheem A, Mahmoud M, Ahmed A (2018) A rigorous data-driven approach to predict Poisson’s ratio. Petrophysics 59:761–777. https://doi.org/10.30632/PJV59N6-2018a2

    Article  Google Scholar 

  • Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system-a survey. Int J Comput Appl 123:32–38. https://doi.org/10.5120/ijca2015905635

    Article  Google Scholar 

  • Wang Y, Salehi S (2015) Drilling hydraulics optimization using neural networks. In: SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, 3-5 March. Society of Petroleum Engineers

  • Wang Q, Ji S, Sun S, Marcotte D (2009) Correlations between compressional and shear wave velocities and corresponding Poisson’s ratios for some common rocks and sulfide ores. Tectonophysics 469:61–72. https://doi.org/10.1016/j.tecto.2009.01.025

    Article  Google Scholar 

  • Wood DA (2020) Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data. J Pet Sci Eng 184:106587. https://doi.org/10.1016/j.petrol.2019.106587

    Article  Google Scholar 

  • Wood DA, Choubineh A (2018) Transparent open-box learning network and artificial neural network predictions of bubble-point pressure compared. Petroleum. 6:375–384. https://doi.org/10.1016/j.petlm.2018.12.001

    Article  Google Scholar 

  • Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods Geomech 36:1636–1650. https://doi.org/10.1002/nag.1066

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank King Fahd University of Petroleum & Minerals (KFUPM) for employing its resources in conducting this work.

Author information

Authors and Affiliations

  1. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Ashraf Ahmed, Salaheldin Elkatatny & Abdulazeez Abdulraheem

Authors
  1. Ashraf Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salaheldin Elkatatny
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdulazeez Abdulraheem
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: S. ElkatatnyMethodology: A. AhmedSoftware: A. AhmedFormal analysis: A. AhmedInvestigation: A. AbdulraheemData curation: S. ElkatatnyWriting - original draft preparation: A. AhmedWriting - review and editing: S. Elkatatny and A. AbdulraheemSupervision: S. Elkatatny and A. AbdulraheemAll authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Salaheldin Elkatatny.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Santanu Banerjee

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmed, A., Elkatatny, S. & Abdulraheem, A. Real-time static Poisson’s ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models. Arab J Geosci 14, 436 (2021). https://doi.org/10.1007/s12517-021-06833-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-021-06833-w

Keywords

Search

Navigation