1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 71, Issue 9
  5. Article
Volume 71 Number 9
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

In oil and gas exploration, it is vital to acquire information about the bottom hole conditions. This is done in the field using wireline logging. The sonic log is one of the most prolific logs as it assists in porosity determination, cement evaluation and identification of lithology and gas‐bearing intervals. However, sonic logging tools are not always a part of the wireline logging arrangement. Still, there are sections where the logging data are missing, and in some cases, these are dependent upon old tools. The tool is incapable of recording shear wave transit times. This study explores the possibility of substituting the sonic log using machine learning and artificial intelligence techniques. These techniques can also predict the sonic log data in sections where these are missing or unreliable. Artificial neural networks, decision tree regression, random forest regression, support vector regression and extreme gradient boosting are the most popular tools available at our disposal for making these estimations. This study has compared these different techniques for their effectiveness and accuracy in making sonic transit time predictions based on other available well logs. The obtained results suggest that despite all the attention on artificial neural networks, eXtreme gradient boosting and the random forest regression outperform it for the given purpose. In the case of missing shear transit time data, random forest regression made predictions with a root mean squared error of 1.03 × 10–4 and a root mean squared error of 0.97 while eXtreme gradient boosting regression did so with a root mean squared error of 1.36 × 10–4 and the same regression coefficient (0.97). When no sonic data were available, random forest regression estimated shear transit time with a root mean squared error of 6.41×10–4 and a regression coefficient of 0.95, and compressional transit time with a root mean squared error of 9.06×10–5 and a root mean squared error of 0.94. The root mean squared error of shear transit time and compressional transit time predictions made using eXtreme gradient boosting were found to be the same as those of random forest regression. The root mean squared error, however, was observed to be slightly less for the compressional transit time predictions and somewhat more for the shear transit time predictions. As data analysis, in general, is a better method for estimation than the use of empirical correlations, these machine learning‐based predictions can serve as powerful tools in the oil and gas exploration industry.

© 2023 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13213
2023-11-10
2025-05-24

  • From This Site

    /content/journals/10.1111/1365-2478.13213
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Abad, A.R.B., Ghorbani, H., Mohamadian, N., Davoodi, S., Mehrad, M., Aghdam, S.K.Y. and Nasriani, H.R. (2022) Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields. Fuel, 308, 121872.
     [Google Scholar]
  2. Agrawal, R., Malik, A., Samuel, R. and Saxena, A. (2022) Real‐time prediction of litho‐facies from drilling data using an artificial neural network: a comparative field data study with optimizing algorithms. Journal of Energy Resources Technology, 144(4), 043003.
     [Google Scholar]
  3. Alford, J., Blyth, M., Tollefsen, E., Crowe, J., Loreto, J., Mohammed, S.et al. (2012) Sonic logging while drilling‐shear answers. Oilfield Review, 24(1), 4–15.
     [Google Scholar]
  4. Anemangely, M., Ramezanzadeh, A., Amiri, H. and Hoseinpour, S.A. (2019) Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 174, 306–327.
     [Google Scholar]
  5. Anemangely, M., Ramezanzadeh, A. and Behboud, M.M. (2019) Geomechanical parameter estimation from mechanical specific energy using artificial intelligence. Journal of Petroleum Science and Engineering, 175, 407–429.
     [Google Scholar]
  6. Anemangely, M., Ramezanzadeh, A. and Tokhmechi, B. (2017) Shear wave travel time estimation from petrophysical logs using ANFIS‐PSO algorithm: a case study from Ab‐Teymour oilfield. Journal of Natural Gas Science and Engineering, 38, 373–387.
     [Google Scholar]
  7. Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., Molaghab, A. and Mohammadian, A. (2018) Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network. Journal of Geophysics and Engineering, 15(4), 1146–1159.
     [Google Scholar]
  8. Ashrafi, S.B., Anemangely, M., Sabah, M. and Ameri, M.J. (2019) Application of hybrid artificial neural networks for predicting rate of penetration (ROP): a case study from Marun oil field. Journal of Petroleum Science and Engineering, 175, 604–623.
     [Google Scholar]
  9. Asoodeh, M. (2013) Prediction of Poisson's ratio from conventional well log data: a committee machine with intelligent systems approach. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 35(10), 962–975.
     [Google Scholar]
  10. Augusto, F.D.O.A. and Martins, J.L. (2009) A well‐log regression analysis for P‐wave velocity prediction in the Namorado oil field, Campos basin. Revista Brasileira de Geofisica, 27(4), 595–608.
     [Google Scholar]
  11. Ben‐Hur, A., Horn, D., Siegelmann, H.T. and Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research, 2(Dec), 125–137.
     [Google Scholar]
  12. Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Oxford: Oxford University Press.
     [Google Scholar]
  13. Carroll, R.D. (1969) The determination of the acoustic parameters of volcanic rocks from compressional velocity measurements. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 6 (6), 557–579.
     [Google Scholar]
  14. Castagna, J.P., Batzle, M.L. and Eastwood, R.L. (1985) Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks. Geophysics, 50(4), 571–581.
     [Google Scholar]
  15. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y. and Cho, H. (2015) Xgboost: extreme gradient boosting. R package version 0.4‐2.
     [Google Scholar]
  16. Choudhary, R. and Gianey, H.K. (2017) Comprehensive review on supervised machine learning algorithms. In 2017 International Conference on Machine Learning and Data Science (MLDS) (pp. 37–43). IEEE.
  17. Claesen, M. and De Moor, B. (2015) Hyperparameter search in machine learning. arXiv:1502.02127.
     [Google Scholar]
  18. Dhar, V., Beg, M., Saxena, A. and Sharma, S. (2021) Capillary suction timer and machine learning techniques as tools for evaluating the performance of different shale inhibitors used in drilling mud. Journal of Natural Gas Science and Engineering, 96, 104301.
     [Google Scholar]
  19. Dietterich, T.G. (2002) Ensemble learning. The Handbook of Brain Theory and Neural Networks, 2, 110–125.
     [Google Scholar]
  20. Doh, C.A. and Alger, R.P. (1958) Sonic logging, a new petrophysical tool. In Rocky Mountain Annual Joint Meeting. Society of Petroleum Engineers.
  21. Elkatatny, S., Tariq, Z., Mahmoud, M., Mohamed, I. and Abdulraheem, A. (2018) Development of new mathematical model for compressional and shear sonic times from wireline log data using artificial intelligence neural networks (white box). Arabian Journal for Science and Engineering, 43(11), 6375–6389.
     [Google Scholar]
  22. Eskandari, H., Rezaee, M.R. and Mohammadnia, M. (2004) Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South‐West Iran. CSEG Recorder, 42, 48.
     [Google Scholar]
  23. Gamal, H., Alsaihati, A. and Elkatatny, S. (2021) Predicting the rock sonic logs while drilling by random forest and decision tree‐based algorithms. Journal of Energy Resources Technology, 144(4), 043203.
     [Google Scholar]
  24. Gevrey, M., Dimopoulos, I. and Lek, S. (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264.
     [Google Scholar]
  25. Ghorbani, H., Wood, D.A., Mohamadian, N., Rashidi, S., Davoodi, S., Soleimanian, A.et al. (2020) Adaptive neuro‐fuzzy algorithm applied to predict and control multi‐phase flow rates through wellhead chokes. Flow Measurement and Instrumentation, 76, 101849.
     [Google Scholar]
  26. Gowida, A. and Elkatatny, S. (2020) Prediction of sonic wave transit times from drilling parameters while horizontal drilling in carbonate rocks using neural networks. Petrophysics‐The SPWLA Journal of Formation Evaluation and Reservoir Description, 61(5), 482–494.
     [Google Scholar]
  27. Gupta, A., Pandey, A., Kesarwani, H., Sharma, S. and Saxena, A. (2021) Automated determination of interfacial tension and contact angle using computer vision for oil field applications. Journal of Petroleum Exploration and Production Technology, 12, 1453–1461.
     [Google Scholar]
  28. Haghighi, M., Shadizadeh, S.R. and Shahbazian, M. (2014) Prediction of compressional and shear slowness from conventional well log data: using intelligent systems. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(19), 2126–2134.
     [Google Scholar]
  29. Harrison, A.R., Randall, C.J., Aron, J.B., Morris, C.F., Wignall, A.H., Dworak, R.A.et al. (1990) Acquisition and analysis of sonic waveforms from a borehole monopole and dipole source for the determination of compressional and shear speeds and their relation to rock mechanical properties and surface seismic data. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
  30. Hartshorn, S. (2016) Machine learning with random forests and decision trees: a visual guide for beginners. Kindle edition.
     [Google Scholar]
  31. Hinton, G.E., Osindero, S. and Teh, Y.W. (2006) A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
     [Google Scholar]
  32. Hsu, K., Brie, A. and Plumb, R.A. (1987) A new method for fracture identification using array sonic tools. Journal of Petroleum Technology, 39(6), 677–683.
     [Google Scholar]
  33. Huang, T.M., Kecman, V. and Kopriva, I. (2006) Kernel‐Based Algorithms for Mining Huge Data Sets (Vol. 1). Heidelberg: Springer.
     [Google Scholar]
  34. Joshi, D., Patidar, A.K., Mishra, A., Mishra, A., Agarwal, S., Pandey, A.et al. (2021) Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles. GeoJournal, 1–22. https://doi.org/10.1007/s10708‐021‐10502‐6
     [Google Scholar]
  35. Khazanehdari, J. and McCann, C. (2005) Acoustic and petrophysical relationships in low‐shale sandstone reservoir rocks. Geophysical Prospecting, 53(4), 447–461.
     [Google Scholar]
  36. Kingma, D.P. and Ba, J. (2014) Adam: a method for stochastic optimization. arXiv:1412.6980.
  37. Krief, M., Garat, J., Stellingwerff, J. and Ventre, J. (1990) A petrophysical interpretation using the velocities of P and S waves (full‐waveform sonic). The Log Analyst, 31(6), SPWLA‐1990‐v31n6a2.
     [Google Scholar]
  38. Lindseth, R.O. (1979) Synthetic sonic logs: a process for stratigraphic interpretation. Geophysics, 44(1), 3–26.
     [Google Scholar]
  39. Liu, S., Zhao, Y. and Wang, Z. (2021) Artificial intelligence method for shear wave travel time prediction considering reservoir geological continuity. Mathematical Problems in Engineering, 2021, 5520428.
     [Google Scholar]
  40. Maleki, S., Moradzadeh, A., Riabi, R.G., Gholami, R. and Sadeghzadeh, F. (2014) Prediction of shear wave velocity using empirical correlations and artificial intelligence methods. NRIAG Journal of Astronomy and Geophysics, 3(1), 70–81.
     [Google Scholar]
  41. Matinkia, M., Amraeiniya, A., Behboud, M.M., Mehrad, M., Bajolvand, M., Gandomgoun, M.H. and Gandomgoun, M. (2022) A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering, 110156.
     [Google Scholar]
  42. Mehrad, M., Bajolvand, M., Ramezanzadeh, A. and Neycharan, J.G. (2020) Developing a new rigorous drilling rate prediction model using a machine learning technique. Journal of Petroleum Science and Engineering, 192, 107338.
     [Google Scholar]
  43. Mehrad, M., Ramezanzadeh, A., Bajolvand, M. and Hajsaeedi, M.R. (2022) Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms. Journal of Petroleum Science and Engineering, 110254.
     [Google Scholar]
  44. Mitchell, T.M. (1997) Machine Learning. New York: McGraw Hill.
     [Google Scholar]
  45. Mohamadian, N., Ghorbani, H., Wood, D.A., Mehrad, M., Davoodi, S., Rashidi, S.et al. (2021) A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning. Journal of Petroleum Science and Engineering, 196, 107811.
     [Google Scholar]
  46. Muqtadir, A., Elkatatny, S.M., Tariq, Z., Mahmoud, M.A. and Abdulraheem, A. (2019) Application of artificial intelligence to predict sonic wave transit time in unconventional tight sandstones. In 53rd US Rock Mechanics/Geomechanics Symposium. OnePetro.
  47. Nair, V. and Hinton, G.E. (2010) Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel
  48. Onalo, D., Adedigba, S., Khan, F., James, L.A. and Butt, S. (2018) Data driven model for sonic well log prediction. Journal of Petroleum Science and Engineering, 170, 1022–1037.
     [Google Scholar]
  49. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.et al. (2011) Scikit‐learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
     [Google Scholar]
  50. Rashidi, S., Mehrad, M., Ghorbani, H., Wood, D.A., Mohamadian, N., Moghadasi, J. and Davoodi, S. (2021) Determination of bubble point pressure and oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms. Journal of Petroleum Science and Engineering, 202, 108425.
     [Google Scholar]
  51. Sabah, M., Mehrad, M., Ashrafi, S.B., Wood, D.A. and Fathi, S. (2021) Hybrid machine learning algorithms to enhance lost‐circulation prediction and management in the Marun oil field. Journal of Petroleum Science and Engineering, 198, 108125.
     [Google Scholar]
  52. Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M. and Younesi, A. (2019) A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Science Informatics, 12(3), 319–339.
     [Google Scholar]
  53. Segal, M.R. (2004) Machine learning benchmarks and random forest regression. UCSF: Center for Bioinformatics and Molecular Biostatistics. Retrieved fromhttps://escholarship.org/uc/item/35x3v9t4
  54. Shahvar, M.B., Badounak, N.D. and Kharrat, R. (2014) A new approach for compressional slowness modeling using wavelet coefficients. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(19), 2106–2112.
     [Google Scholar]
  55. Silva, M.B.C., dos Santos, R.V., Martins, J.L. and Fontoura, S.A. (2001) Sonic log prediction using artificial neural networks: application to Namorado Reservoir Data, Campos Basin, Brazil. In 7th International Congress of the Brazilian Geophysical Society (pp. cp–217). European Association of Geoscientists and Engineers.
  56. Singh, A., Thakur, N. and Sharma, A. (March). A (2016) review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1310–1315). IEEE.
  57. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
     [Google Scholar]
  58. Tabari, K., Tabari, O. and Tabari, M. (2011) A fast method for estimating shear wave velocity by using neural network. Australian Journal of Basic and Applied Sciences, 5, 1429–1434.
     [Google Scholar]
  59. Tariq, Z., Elkatatny, S., Mahmoud, M. and Abdulraheem, A. (2016) A new artificial intelligence based empirical correlation to predict sonic travel time. In International Petroleum Technology Conference. OnePetro.
  60. Vert, J.P., Tsuda, K. and Schölkopf, B. (2004) A primer on kernel methods. Kernel methods in computational biology, 47, 35–70.
     [Google Scholar]
  61. Williams, D.M. (1990) The acoustic log hydrocarbon indicator. In SPWLA 31st Annual Logging Symposium. Society of Petrophysicists and Well‐Log Analysts.
  62. Xu, M., Watanachaturaporn, P., Varshney, P.K. and Arora, M.K. (2005) Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322–336.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13213
Loading
Augmenting and eliminating the use of sonic logs using artificial intelligence: A comparative evaluation
Geophysical Prospecting 71, 1933 (2023); https://doi.org/10.1111/1365-2478.13213
/content/journals/10.1111/1365-2478.13213
/content/journals/10.1111/1365-2478.13213
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Artificial intelligence; Compressional wave; Neural networks; Shear wave; Sonic log

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error