Neural Computing and Applications

, Volume 31, Issue 12, pp 8561–8581 | Cite as

Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

  • Zeeshan TariqEmail author
  • Mohamed Mahmoud
  • Abdulazeez Abdulraheem
Original Article


Current oil prices and global financial situations underline the need for the best engineering practices to recover remaining hydrocarbons. A good understanding of the elastic behavior of the reservoir rock is extremely imperative in avoiding the severe well drilling problems such as wellbore in-stability, differential sticking, kicks, and many more. Therefore, it is plausible to have a good estimation of the rock elastic behavior for successful well operations. This study presents a generalized empirical model to predict static Poisson’s ratio of the carbonate rocks. Petrophysical well logs were used as inputs, and the laboratory measured static Poisson’s ratio was used as an output. Three supervised artificial intelligence (AI) techniques were used, viz. artificial neural network (ANN), support vectors regression, and adaptive network-based fuzzy interference system. An extensive prediction comparison was made between these three AI techniques. Based on the lowest average absolute percentage error (AAPE) and highest coefficient of determination (R2), the ANN model proposed to be the best model to predict static Poisson’s ratio. To transform black box nature of AI model into a white box, ANN-based empirical correlation is also developed to predict the static Poisson’s ratio. Comparison of the developed empirical correlation with previously established approaches to find static Poisson’s ratio on an unseen published dataset revealed that the equation of ANN can predict the static Poisson’s ratio with implicitly less AAPE and with high R2 value. The proposed model with the empirical correlation can assist geo-mechanical engineers to predict the static Poisson’s ratio in the absence of core data. The novelty of the new equation is that it can be used without the need of any AI software.


Static Poisson’s ratio Carbonate rocks Triaxial tests Well logs Artificial intelligence Particle swarm optimization Mathematical model 



Absolute percentage error


Average absolute percentage error


Adaptive neuro-fuzzy inference system


Artificial neural network


Correlation coefficient


Feedforward neural network


Linear variable differential transducer


Multilayer perceptron


Poisson’s ratio


Radial basis function


Root mean square error


Support vectors regression


Unconfined compressive strength

List of symbols


Bias between input and hidden layer of neural network


Bias between hidden and output layer of neural network


Cognitive parameter \(\left( {0 \le c_{1} \le 1.2} \right)\)


Cognitive parameter \(\left( {0 \le c_{2} \le 1.2} \right)\)


Dynamic Young’s modulus (MPsi)


Static Young’s modulus (MPsi)


Dynamic Young’s modulus (MPsi)


Index for neurons


Index for number of input parameters


Iteration number


Total number of neurons


Dynamic Poisson’s ratio


Static Poisson’s ratio


Compressional wave


Particle \(i\) position at any iteration


Particle best solution


Global best solution


Bulk density (g/cc)


Coefficient of determination


Shear wave


Weight \(\left( {0 \le w \le 1.2} \right)\)


Weight \(\left( {0 \le w \le 1.2} \right)\)


Weights vector between input and hidden layer of neural network


Weights vector between hidden and output layer of neural network


Input parameters


Output variable


Activation function between hidden and output layer of FFNN


Activation function between input and hidden layer of FFNN


Compressional wave transit time (µs/ft)


S-wave transit time (µs/ft)


Bulk density (g/cc)


Dynamic Poisson’s ratio



The authors would like to acknowledge College of Petroleum & Geosciences (CPG), King Fahd University of Petroleum & Minerals for providing research opportunities to produce this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    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. CrossRefGoogle Scholar
  2. 2.
    Anifowose F, Adeniye S, Abdulraheem A, Al-Shuhail A (2016) Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models. J Pet Sci Eng 145:230–237. CrossRefGoogle Scholar
  3. 3.
    Anifowose FA, Labadin J, Abdulraheem A (2015) Ensemble model of non-linear feature selection-based extreme learning machine for improved natural gas reservoir characterization. J Nat Gas Sci Eng 26:1561–1572. CrossRefGoogle Scholar
  4. 4.
    Anifowose FA, Labadin J, Abdulraheem A (2017) Ensemble machine learning: an untapped modeling paradigm for petroleum reservoir characterization. J Pet Sci Eng 151:480–487. CrossRefGoogle Scholar
  5. 5.
    Helmy T, Hossain MI, Adbulraheem A et al (2017) Prediction of non-hydrocarbon gas components in separator by using hybrid computational intelligence models. Neural Comput Appl 28:635–649. CrossRefGoogle Scholar
  6. 6.
    Al-Bulushi NI, King PR, Blunt MJ, Kraaijveld M (2012) Artificial neural networks workflow and its application in the petroleum industry. Neural Comput Appl 21:409–421. CrossRefGoogle Scholar
  7. 7.
    Mohaghegh S (1995) Neural network: what it can do for petroleum engineers. J Pet Technol 47:42. CrossRefGoogle Scholar
  8. 8.
    Elkatatny S, Mahmoud M, Tariq Z, Abdulraheem A (2017) New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network. Neural Comput Appl. CrossRefGoogle Scholar
  9. 9.
    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 ConferenceGoogle Scholar
  10. 10.
    Najibi AR, Ghafoori M, Lashkaripour GR, Asef MR (2015) Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran. J Pet Sci Eng 126:78–82. CrossRefGoogle Scholar
  11. 11.
    Abdulraheem A, Sabakhy E, Ahmed M et al (2007) Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic. In: SPE middle east oil and gas show and conference. Society of Petroleum EngineersGoogle Scholar
  12. 12.
    Nooruddin HA, Anifowose F, Abdulraheem A (2013) Applying artificial intelligence techniques to develop permeability predictive models using mercury injection capillary-pressure data. In: SPE Saudi Arabia section technical symposium and exhibition. Society of Petroleum EngineersGoogle Scholar
  13. 13.
    Anifowose F, Labadin J, Abdulraheem A (2013) A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction. Neural Comput Appl 23:179–190. CrossRefGoogle Scholar
  14. 14.
    Helmy T, Rahman SM, Hossain MI, Abdelraheem A (2013) Non-linear heterogeneous ensemble model for permeability prediction of oil reservoirs. Arab J Sci Eng 38:1379–1395. CrossRefGoogle Scholar
  15. 15.
    Shujath Ali S, Hossain ME, Hassan MR, Abdulraheem A (2013) Hydraulic unit estimation from predicted permeability and porosity using artificial intelligence techniques. In: North Africa technical conference and exhibition. Society of Petroleum EngineersGoogle Scholar
  16. 16.
    Abdulraheem A, Ahmed M, Vantala A, Parvez T (2009) Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. In: SPE Saudi Arabia section Technical Symposium. Society of Petroleum EngineersGoogle Scholar
  17. 17.
    Tariq Z, Elkatatny S, Mahmoud M, Abdulraheem A (2016) A holistic approach to develop new rigorous empirical correlation for static Young’s Modulus. In: Abu Dhabi international petroleum exhibition & conference. Society of Petroleum EngineersGoogle Scholar
  18. 18.
    Tariq Z, Elkatatny S, Mahmoud M et al (2017) A new approach to predict failure parameters of carbonate rocks using artificial intelligence tools. In: SPE Kingdom of Saudi Arabia annual technical symposium and exhibition. Society of Petroleum EngineersGoogle Scholar
  19. 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. Society of Petroleum EngineersGoogle Scholar
  20. 20.
    Yang Y, Rosenbaum MS (2002) The artificial neural network as a tool for assessing geotechnical properties. Geotech Geol Eng 20:149–168. CrossRefGoogle Scholar
  21. 21.
    Sonmez H, Tuncay E, Gokceoglu C (2004) Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate. Int J Rock Mech Min Sci 41:717–729. CrossRefGoogle Scholar
  22. 22.
    Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 11:2587–2594. CrossRefGoogle Scholar
  23. 23.
    Elkatatny S, Tariq Z, Mahmoud M 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. CrossRefGoogle Scholar
  24. 24.
    Bazargan H, Adibifard M (2017) A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter. Neural Comput Appl. CrossRefGoogle Scholar
  25. 25.
    Artun E (2017) Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study. Neural Comput Appl 28:1729–1743. CrossRefGoogle Scholar
  26. 26.
    Fattahi H, Gholami A, Amiribakhtiar MS, Moradi S (2015) Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search. Neural Comput Appl 26:789–798. CrossRefGoogle Scholar
  27. 27.
    Alimohammadi S, Sayyad Amin J, Nikooee E (2017) Estimation of asphaltene precipitation in light, medium and heavy oils: experimental study and neural network modeling. Neural Comput Appl 28:679–694. CrossRefGoogle Scholar
  28. 28.
    Adebayo AR, Abdulraheem A, Olatunji SO (2015) Artificial intelligence based estimation of water saturation in complex reservoir systems. J Porous Media 18:893–906. CrossRefGoogle Scholar
  29. 29.
    Baziar S, Shahripour HB, Tadayoni M, Nabi-Bidhendi M (2016) Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study. Neural Comput Appl. 10:15–20. CrossRefGoogle Scholar
  30. 30.
    Gatens JM, Harrison CW, Lancaster DE, Guidry FK (1990) In-situ stress tests and acoustic logs determine mechanical propertries and stress profiles in the devonian shales. SPE Form Eval 5:248–254. CrossRefGoogle Scholar
  31. 31.
    Chang C, Zoback MD, Khaksar A (2006) Empirical relations between rock strength and physical properties in sedimentary rocks. J Pet Sci Eng 51:223–237. CrossRefGoogle Scholar
  32. 32.
    Khaksar A, Taylor PG, Fang Z et al (2009) Rock strength from core and logs, where we stand and ways to go. In: EUROPEC/EAGE conference and exhibition. Society of Petroleum EngineersGoogle Scholar
  33. 33.
    Nes O-M, Fjær E, Tronvoll J et al (2005) Drilling time reduction through an integrated rock mechanics analysis. In: SPE/IADC drilling conference. Society of Petroleum EngineersGoogle Scholar
  34. 34.
    Chan T, Hood M, Board M (1982) Rock properties and their effect on thermally induced displacements and stresses. J Energy Resour Technol 104:384. CrossRefGoogle Scholar
  35. 35.
    Cadwallader S, Wampler J, Sun T et al (2015) An integrated dataset centered around distributed fiber optic monitoring—key to the successful implementation of a geo-engineered completion optimization program in the eagle ford shale. In: Proceedings of the 3rd unconventional resources technology conference. American Association of Petroleum Geologists, Tulsa, OK, USAGoogle Scholar
  36. 36.
    Wang C, Wu Y-S, Xiong Y et al (2015) Geomechanics coupling simulation of fracture closure and its influence on gas production in shale gas reservoirs. In: SPE reservoir simulation symposium. Society of Petroleum EngineersGoogle Scholar
  37. 37.
    Nawrocki PA, Dusseault MB (1996) Modelling of damaged zones around boreholes using a radius dependent Young’S modulus. J Can Pet Technol. CrossRefGoogle Scholar
  38. 38.
    Ameen MS, Smart BGD, Somerville JM et al (2009) Predicting rock mechanical properties of carbonates from wireline logs (a case study: arab-D reservoir, Ghawar field, Saudi Arabia). Mar Pet Geol 26:430–444. CrossRefGoogle Scholar
  39. 39.
    Elkatatny S, Tariq Z, Mahmoud M et al (2018) An integrated approach for estimating static Young’s modulus using artificial intelligence tools. Neural Comput Appl. CrossRefGoogle Scholar
  40. 40.
    Tariq Z, Elkatatny SM, Mahmoud MA, Abdulraheem A, Abdelwahab AZ, Woldeamanuel M (2017) Estimation of rock mechanical parameters using artificial intelligence tools. American Rock Mechanics AssociationGoogle Scholar
  41. 41.
    Mahmoud M, Elkatatny S, Ramadan E, Abdulraheem A (2016) Development of lithology-based static Young’s modulus correlations from log data based on data clustering technique. J Pet Sci Eng 146:10–20. CrossRefGoogle Scholar
  42. 42.
    Tariq Z, Elkatatny SM, Mahmoud MA et al (2017) Development of new correlation of unconfined compressive strength for carbonate reservoir using artificial intelligence techniques. In: 51st US rock mechanics/geomechanics symposium 2017Google Scholar
  43. 43.
    D’Andrea D V., Fischer RL, Fogelson DE (1965) Prediction of compressive strength from other rock properties. United States Department of The Interior Bureau of MinesGoogle Scholar
  44. 44.
    Kumar A, Jayakumar T, Raj B, Ray KK (2003) Correlation between ultrasonic shear wave velocity and Poisson’s ratio for isotropic solid materials. Acta Mater 51:2417–2426. CrossRefGoogle Scholar
  45. 45.
    Phani KK (2008) Correlation between ultrasonic shear wave velocity and Poisson’s ratio for isotropic porous materials. J Mater Sci 43:316–323. CrossRefGoogle Scholar
  46. 46.
    Edimann K, Somerville JM, Smart BGD et al (1998) Predicting rock mechanical properties from wireline porosities. In: SPE/ISRM rock mechanics in petroleum engineering. Society of Petroleum EngineersGoogle Scholar
  47. 47.
    Al-Shayea NA (2004) Effects of testing methods and conditions on the elastic properties of limestone rock. Eng Geol 74:139–156. CrossRefGoogle Scholar
  48. 48.
    Singh V, Singh TN (2006) A neuro-fuzzy approach for prediction of Poisson’s ratio and young’s modulus of shale and sandstone. In: The 41st U.S. symposium on rock mechanics (USRMS), 17–21 June, Golden, ColoradoGoogle Scholar
  49. 49.
    Shalabi FI, Cording EJ, Al-Hattamleh OH (2007) Estimation of rock engineering properties using hardness tests. Eng Geol 90:138–147. CrossRefGoogle Scholar
  50. 50.
    Al-Anazi A, Gates ID (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng Geol 114:267–277. CrossRefGoogle Scholar
  51. 51.
    Baykasoğlu A, Dereli T, Tanış S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34:2083–2090. CrossRefGoogle Scholar
  52. 52.
    ASTM D-04 (2005) Standard test method for triaxial compressive strength of undrained rock core specimens without pore pressure measurementsGoogle Scholar
  53. 53.
    Rao S, 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. IEEE, pp 52–57Google Scholar
  54. 54.
    Angelini E, Ludovici A (2009) CDS Evaluation model with neural networks. J Serv Sci Manag 02:15–28. CrossRefGoogle Scholar
  55. 55.
    Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453. CrossRefGoogle Scholar
  56. 56.
    Hinton GE, Osindero S, Teh Y-W (2006) A Fast Learning Algorithm For Deep Belief Nets. Neural Comput 18:1527–1554. MathSciNetCrossRefzbMATHGoogle Scholar
  57. 57.
    Lippmann R (1994) Book review: “Neural Networks, A Comprehensive Foundation”, by Simon Haykin. Int J Neural Syst 05:363–364. CrossRefGoogle Scholar
  58. 58.
    Vineis P, Rainoldi A (1997) Neural networks and logistic regression: analysis of a case-control study on myocardial infarction. J Clin Epidemiol 50:1309–1310. CrossRefGoogle Scholar
  59. 59.
    Yılmaz I, Yuksek AG (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41:781–795. CrossRefGoogle Scholar
  60. 60.
    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. CrossRefGoogle Scholar
  61. 61.
    Mohaghegh SD (2017) Shale Analytics. Springer, ChamCrossRefGoogle Scholar
  62. 62.
    Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. CrossRefGoogle Scholar
  63. 63.
    Jang J-SR (1996) Input selection for ANFIS learning. In: Proceedings of IEEE 5th international fuzzy systems. IEEE, pp 1493–1499Google Scholar
  64. 64.
    Jang J-SR, Sun Chuen-Tsai (1995) Neuro-fuzzy modeling and control. Proc IEEE 83:378–406. CrossRefGoogle Scholar
  65. 65.
    Ebrahimi M, Sajedian A (2010) Use of fuzzy logic for predicting two phase inflow performance relationship of horizontal oil wells. In: Trinidad and tobago energy resources conference. Society of Petroleum EngineersGoogle Scholar
  66. 66.
    Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system—a survey. Int J Comput Appl 123:32–38. CrossRefGoogle Scholar
  67. 67.
    Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18–27. CrossRefGoogle Scholar
  68. 68.
    El-Sebakhy EA, Hadi AS, Faisal KA (2007) Iterative least squares functional networks classifier. IEEE Trans Neural Networks 18:844–850. CrossRefGoogle Scholar
  69. 69.
    Elhaj MA, Anifowose F, Abdulraheem A, Fahad K (2015) Single gas flow prediction through chokes using artificial intelligence techniques. SPE Saudi Arabia Section Annual Technical Symposium and Exhibition, 21–23 April, Al-Khobar, Saudi Arabia.
  70. 70.
    Anifowose F, Adeniye S, Abdulraheem A (2014) Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study. J Exp Theor Artif Intell 26:551–570. CrossRefGoogle Scholar
  71. 71.
    Trontl K, Šmuc T, Pevec D (2007) Support vector regression model for the estimation of γ-ray buildup factors for multi-layer shields. Ann Nucl Energy 34:939–952. CrossRefGoogle Scholar
  72. 72.
    Jeng J-T, Chuang C-C, Su S-F (2003) Support vector interval regression networks for interval regression analysis. Fuzzy Sets Syst 138:283–300. MathSciNetCrossRefzbMATHGoogle Scholar
  73. 73.
    Khoukhi A, Oloso M, Elshafei M et al (2011) Support vector regression and functional networks for viscosity and gas/oil ratio curves estimation. Int J Comput Intell Appl 10:269–293. CrossRefGoogle Scholar
  74. 74.
    Guo G (2014) Support vector machines applications. Springer, ChamGoogle Scholar
  75. 75.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39–43Google Scholar
  76. 76.
    Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of 1997 IEEE international conference on evolutionary computation (ICEC’97). IEEE, pp 303–308Google Scholar
  77. 77.
    Tariq Z, Abdulraheem A, Khan MR, Sadeed A (2018) New inflow performance relationship for a horizontal well in a naturally fractured solution gas drive reservoirs using artificial intelligence technique. In: Offshore technology conference Asia. Offshore Technology ConferenceGoogle Scholar
  78. 78.
    Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: International conference on evolutionary programming, pp 611–616Google Scholar
  79. 79.
    Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5:989–993. CrossRefGoogle Scholar
  80. 80.
    Bello O, Asafa T (2014) A functional networks softsensor for flowing bottomhole pressures and temperatures in multiphase production wells. In: SPE intelligent energy conference & exhibition. Society of Petroleum EngineersGoogle Scholar
  81. 81.
    Awadalla M, Yousef H (2016) Neural networks for flow bottom hole pressure prediction. Int J Electr Comput Eng 6:1839. CrossRefGoogle Scholar
  82. 82.
    Memon PQ, Yong S-P, Pao W, Sean PJ (2014) Surrogate reservoir modeling-prediction of bottom-hole flowing pressure using radial basis neural network. In: 2014 Science and information conference. IEEE, pp 499–504Google Scholar
  83. 83.
    Jahanandish I, Salimifard B, Jalalifar H (2011) Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks. J Pet Sci Eng 75:336–342. CrossRefGoogle Scholar
  84. 84.
    Osman E-SA, Ayoub MA, Aggour MA (2005) An artificial neural network model for predicting bottomhole flowing pressure in vertical multiphase flow. In: SPE middle east oil and gas show and conference. Society of Petroleum EngineersGoogle Scholar
  85. 85.
    Ebrahimi A, Khamehchi E (2015) A robust model for computing pressure drop in vertical multiphase flow. J Nat Gas Sci Eng 26:1306–1316. CrossRefGoogle Scholar
  86. 86.
    Adebayo AR, Abdulraheem A, Al-Shammari AT (2013) Promises of artificial intelligence techniques in reducing errors in complex flow and pressure losses calculations in multiphase fluid flow in oil wells. In: SPE Nigeria annual international conference and exhibition. Society of Petroleum EngineersGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Petroleum EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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