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

Although lithofacies routinely is featured by distinct logging responses from each other, many types of lithofacies in practical cases show similar measuring characteristics on logs, and then to achieve a desirable solution from logging‐based lithofacies prediction actually is challengeable. Since the mathematical essence of lithofacies prediction can be explained as an issue of pattern recognition, a light gradient boosting machine, a state‐of‐the‐art ensemble learning, specifically developed to address supervised classification, could be a potential solver. Nonetheless, due to an incompatibility of inherent exclusive feature bundling algorithm for logs and usage of a great deal of hyper‐parameters, a raw light gradient boosting machine might not be suitable or functional to predict lithofacies. Thus, continuous restricted Boltzmann machine and Bayesian optimization, respectively, employed to process original logging data and optimize hyper‐parameters, are adopted as technical assistants for the light gradient boosting machine, and accordingly a new ensemble learning‐based predictor called continuous restricted Boltzmann machine – Bayesian optimization – the light gradient boosting machine, is proposed for lithofacies. To validate classifying capability and robust nature of new predictor, three experiments are designed purposefully based on the application of a dataset collected from the wells located within pre‐salt lacustrine carbonate reservoirs of the Santos Basin. Simultaneously, to highlight validating effect, other three sophisticated classifiers are introduced as competitors in all experiments, including supper vector machine, random forest and extreme gradient boosting. According to the analysis and comparison of all experimental results, overall four points are verified: (1) the integration of continuous restricted Boltzmann machine and Bayesian optimization indeed is beneficial for the light gradient boosting machine in data preprocess and modelling stage; (2) the light gradient boosting machine cored predictor shows more capability of producing reliable predicted lithofacies information compared to other three competitors; (3) training more learning samples is a simple and effective approach to enhance computing capability of any validated predictor, and under this circumstance the light gradient boosting machine cored predictor still performs relatively better; (4) the light gradient boosting machine cored predictor is characterized with a better robustness as its predicting outcomes, even derived from a sparse‐condition dataset, are also proved more qualified. Consequently, the new proposed predictor is demonstrated as a high‐efficient and robust solver for the prediction of lithofacies and deserves a widespread application in the geological, geophysical and petrophysical research.

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

Article metrics loading...

/content/journals/10.1111/1365-2478.13258
2023-11-10
2025-03-21

  • From This Site

    /content/journals/10.1111/1365-2478.13258
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Al‐Anazi, A. & Gates, I.D. (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Engineering Geology, 114(3–4), 267–277.
     [Google Scholar]
  2. Al‐Mudhafar, W.J. (2020) Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs. Journal of Petroleum Science and Engineering, 195, 107837.
     [Google Scholar]
  3. Alves, T.M., Fetter, M., Lima, C., Cartwright, J.A., Cosgrove, J., Gangá, A. & Strugale, M. (2017) An incomplete correlation between pre‐salt topography, top reservoir erosion, and salt deformation in deep‐water Santos Basin (SE Brazil). Marine and Petroleum Geology, 79, 300–320.
     [Google Scholar]
  4. Anemangely, M., Ramezanzadeh, A. & 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 & Engineering, 38, 373–387.
     [Google Scholar]
  5. Antariksa, G., Muammar, R. & Lee, J. (2022) Performance evaluation of machine learning‐based classification with rock‐physics analysis of geological lithofacies in Tarakan Basin, Indonesia. Journal of Petroleum Science and Engineering, 208, 109250.
     [Google Scholar]
  6. Azerêdo, A.C., Duarte, L.V. & Silva, A.P. (2021) The challenging carbonates from the pre‐salt reservoirs offshore Brazil: facies, palaeoenvironment and diagenesis. Journal of South American Earth Sciences, 108, 103202.
     [Google Scholar]
  7. Bergstra, J. & Bengio, Y. (2012) Random search for hyper‐parameter optimization. Journal of Machine Learning Research, 13, 281–305.
     [Google Scholar]
  8. Bhattacharya, S., Carr, T.R. & Pal, M. (2016) Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: case studies from the Bakken and Mahantango‐Marcellus Shale, USA. Journal of Natural Gas Science and Engineering, 33, 1119–1133.
     [Google Scholar]
  9. Böken, B. (2021) On the appropriateness of Platt scaling in classifier calibration. Information Systems, 95, 101641.
     [Google Scholar]
  10. Breiman, L. (2001) Random forests. Machine Learning, 45(1), 5–32.
     [Google Scholar]
  11. Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
     [Google Scholar]
  12. Caruana, R. & Niculescu‐Mizil, A. (2006) An empirical comparison of supervised learning algorithms. In: ACM international conference proceeding series, vol. 148. New York: Association for Computing Machinery, pp. 161–168.
     [Google Scholar]
  13. Chen, H. & Murray, A.F. (2003) Continuous restricted Boltzmann machine with an implementable training algorithm. IEEE Proceedings: Vision, Image and Signal Processing, 150(3), 153–159.
     [Google Scholar]
  14. Chen, T. & Guestrin, C. (2016) XGBoost: a scalable tree boosting system. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. New York: Association for Computing Machinery, pp. 785–794.
  15. Chen, Y., Lu, L. & Li, X. (2014) Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140, 56–63.
     [Google Scholar]
  16. Chicco, D. & Jurman, G. (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6.
     [Google Scholar]
  17. Clerc, M. & Kennedy, J. (2002) The particle swarm‐explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.
     [Google Scholar]
  18. Comon, P. (1994) Independent component analysis, a new concept?Signal Processing, 36(3), 287–314.
     [Google Scholar]
  19. Dekel, O., Gilad‐Bachrach, R., Shamir, O. & Xiao, L. (2012) Optimal distributed online prediction using mini‐batches. Journal of Machine Learning Research, 13, 165–202.
     [Google Scholar]
  20. Derrac, J., García, S., Molina, D. & Herrera, F. (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.
     [Google Scholar]
  21. Dunham, R.J. (1962) Classification of carbonate rocks according to depositional texture. In: Ham, W.E. (Ed.) Classification of carbonate rocks, vol. 1, Memoir. Tulsa, OK: American Association of Petroleum Geologists, pp. 108–122.
     [Google Scholar]
  22. Fawcett, T. (2006) An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
     [Google Scholar]
  23. Feng, R. (2020) Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models. Geophysical Journal International, 221(3), 1484–1498.
     [Google Scholar]
  24. Feng, R. (2021) Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm. Journal of Petroleum Science and Engineering, 196, 107995.
     [Google Scholar]
  25. Friedman, J.H. (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
     [Google Scholar]
  26. Gibbs, P.B., Brush, E.R. & Fiduk, J.C. (2003) The evolution of the syn‐rift and transition phases of the central/southern Brazilian and W. African conjugate margins: the implications for source rock distribution in time and space, and their recognition on seismic data. In 8th International Congress of the Brazilian Geophysical Society. Utrecht: European Association of Geoscientists & Engineers, pp. 1–6.
     [Google Scholar]
  27. Golmohammadi, A. & Jafarpour, B. (2020) Reducing uncertainty in conceptual prior models of complex geologic systems via integration of flow response data. Computational Geosciences, 24(1), 161–180.
     [Google Scholar]
  28. Gomes, J.P., Bunevich, R.B., Tedeschi, L.R., Tucker, M.E. & Whitaker, F.F. (2020) Facies classification and patterns of lacustrine carbonate deposition of the Barra Velha Formation, Santos Basin, Brazilian Pre‐salt. Marine and Petroleum Geology, 113, 104176.
     [Google Scholar]
  29. Goodsell, D.S., Morris, G.M. & Olson, A.J. (1996) Automated docking of flexible ligands: applications of AutoDock. Journal of Molecular Recognition, 9(1), 1–5.
     [Google Scholar]
  30. Hinton, G.E., Osindero, S. & Teh, Y.W. (2006) A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
     [Google Scholar]
  31. Huang, H.B., Li, R.X., Yang, M.L., Lim, T.C. & Ding, W.P. (2017) Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine‐based DBN. Mechanical Systems and Signal Processing, 84, 245–267.
     [Google Scholar]
  32. Jasmin, M., Vigneshwaran, T. & Beulah, H.S. (2015) Design of power aware on chip embedded memory based FSM encoding in FPGA. International Journal of Applied Engineering Research, 10(2), 4487–4496.
     [Google Scholar]
  33. Jervis, M., Liu, M. & Smith, R. (2021) Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification. Leading Edge, 40(7), 514–523.
     [Google Scholar]
  34. Jones, D.R., Schonlau, M. & Welch, W.J. (1998) Efficient global optimization of expensive black‐box functions. Journal of Global Optimization, 13(4), 455–492.
     [Google Scholar]
  35. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R. & Wu, A.Y. (2002) An efficient k‐means clustering algorithms: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892.
     [Google Scholar]
  36. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W. & Liu, T.Y. (2017) LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
     [Google Scholar]
  37. Kennedy, J. & Eberhart, R.C. (1995) Particle swarm optimization. IEEE International Conference on Neural Networks, 4, 1942–1948.
     [Google Scholar]
  38. Matinkia, M., Amraeiniya, A., Behboud, M.M., Mehrad, M., Bajolvand, M., Gandomgoun, M.H. & Gandomgoun, M. (2022a) A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering, 211, 110156.
     [Google Scholar]
  39. Matinkia, M., Hashami, R., Mehrad, M., Hajsaeedi, M.R. & Velayati, A. (2022b) Prediction of permeability from well logs using a new hybrid machine learning algorithm. Petroleum. Available from: https://doi.org/10.1016/j.petlm.2022.03.003
     [Google Scholar]
  40. Matinkia, M., Sheykhinasab, A., Shojaei, S., Tazeh Kand, A.V., Elmi, A., Bajolvand, M. & Mehrad, M. (2022c) Developing a new model for drilling rate of penetration prediction using convolutional neural network. Arabian Journal for Science and Engineering. Available from: https://doi.org/10.1007/s13369‐022‐06765‐x
     [Google Scholar]
  41. Mehrad, M., Ramezanzadeh, A., Bajolvand, M. & Reza Hajsaeedi, M. (2022) Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms. Journal of Petroleum Science and Engineering, 212, 110254.
     [Google Scholar]
  42. Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P. & Kapur, T. (2020) Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Transactions on Medical Imaging, 39(12), 3868–3878.
     [Google Scholar]
  43. Merembayev, T., Kurmangaliyev, D., Bekbauov, B. & Amanbek, Y. (2021) A comparison of machine learning algorithms in predicting lithofacies: case studies from Norway and Kazakhstan. Energies, 14(7), 1896.
     [Google Scholar]
  44. Mirjalili, S. & Lewis, A. (2016) The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
     [Google Scholar]
  45. Møller, M.F. (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525–533.
     [Google Scholar]
  46. Moradi, M., Tokhmechi, B. & Masoudi, P. (2019) Inversion of well logs into rock types, lithofacies and environmental facies, using pattern recognition, a case study of carbonate Sarvak Formation. Carbonates and Evaporites, 34(2), 335–347.
     [Google Scholar]
  47. Moreira, J.L.P., Madeira, C.V., Gil, J.A. & Machado, M.A.P. (2007) Bacia de Santos. Bol. Geociencias Petrobras, 15(2), 531–549 (in Portuguese).
     [Google Scholar]
  48. Niculescu‐Mizil, A. & Caruana, R. (2005) Predicting good probabilities with supervised learning. In Proceedings of the 22nd international conference on machine learning. New York: Association for Computing Machinery, pp. 625–632.
  49. Ozkan, A., Cumella, S.P., Milliken, K.L. & Laubach, S.E. (2011) Prediction of lithofacies and reservoir quality using well logs, Late Cretaceous Williams Fork Formation, Mamm Creek field, Piceance Basin, Colorado. AAPG Bulletin, 95(10), 1699–1724.
     [Google Scholar]
  50. Perez, H.H., Datta‐Gupta, A. & Mishra, S. (2005) The role of electrofacies, lithofacies, and hydraulic flow units in permeability prediction from well logs: a comprative analysis using classification trees. SPE Reservoir Evaluation and Engineering, 8(2), 143–155.
     [Google Scholar]
  51. Pietzsch, R., Oliveira, D.M., Tedeschi, L.R., Queiroz Neto, J.V., Figueiredo, M.F., Vazquez, J.C. & de Souza, R.S. (2018) Palaeohydrology of the Lower Cretaceous pre‐salt lacustrine system, from rift to post‐rift phase, Santos Basin, Brazil. Palaeogeography, Palaeoclimatology, Palaeoecology, 507, 60–80.
     [Google Scholar]
  52. Qi, L. & Carr, T.R. (2006) Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas. Computers and Geosciences, 32(7), 947–964.
     [Google Scholar]
  53. Salanghouch, M.S. & Niri, M.E. (2021) Reservoir lithofacies modeling using well logs and seismic data based on sequential indicator simulations and probability perturbation method in a Bayesian framework. Geopersia, 11(1), 153–168.
     [Google Scholar]
  54. Schweidtmann, A.M., Bongartz, D., Grothe, D., Kerkenhoff, T., Lin, X., Najman, J. & Mitsos, A. (2021) Deterministic global optimization with Gaussian processes embedded. Mathematical Programming Computation, 13(3), 553–581.
     [Google Scholar]
  55. Sebtosheikh, M.A., Motafakkerfard, R., Riahi, M.A., Moradi, S. & Sabety, N. (2015) Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs. Carbonates and Evaporites, 30(1), 59–68.
     [Google Scholar]
  56. Shi, Y. & Eberhart, R.C. (1999) Empirical study of particle swarm optimization. In: Proceedings of the Congress on evolutionary computation, vol. 3. Piscataway, NJ: IEEE, pp. 1945–1950.
     [Google Scholar]
  57. Sokolova, M. & Lapalme, G. (2009) A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437.
     [Google Scholar]
  58. Song, Q.J., Jiang, H.Y. & Liu, J. (2017) Feature selection based on FDA and F‐score for multi‐class classification. Expert Systems with Applications, 81, 22–27.
     [Google Scholar]
  59. Specht, D.F. (1990) Probabilistic neural networks. Neural Networks, 3(1), 109–118.
     [Google Scholar]
  60. Sun, Z., Jiang, B., Li, X., Li, J. & Xiao, K. (2020) A data‐driven approach for lithology identification based on parameter‐optimized ensemble learning. Energies, 13(15), 3903.
     [Google Scholar]
  61. Tang, H., Toomey, N. & Meddaugh, W.S. (2011) Using an artificial‐neural‐network method to predict carbonate well log facies successfully. SPE Reservoir Evaluation and Engineering, 14(1), 35–44.
     [Google Scholar]
  62. Tenenbaum, J.B., De Silva, V. & Langford, J.C. (2000) A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.
     [Google Scholar]
  63. Tewari, S. & Dwivedi, U.D. (2019) Ensemble‐based big data analytics of lithofacies for automatic development of petroleum reservoirs. Computers and Industrial Engineering, 128, 937–947.
     [Google Scholar]
  64. Tewari, S. & Dwivedi, U.D. (2020) A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies. Journal of Petroleum Exploration and Production Technology, 10(5), 1849–1868.
     [Google Scholar]
  65. Thompson, D.L., Stilwell, J.D. & Hall, M. (2015) Lacustrine carbonate reservoirs from early cretaceous rift lakes of western Gondwana: pre‐salt coquinas of Brazil and west Africa. Gondwana Research, 28(1), 26–51.
     [Google Scholar]
  66. Tukey, J.W. (1975) Mathematics and the picturing of data. In: Proceedings of the International Congress of Mathematicians. Vancouver: Canadian Mathematical Congress, pp. 523–531.
  67. Varejão, F.G., Warren, L.V., Simões, M.G., Buatois, L.A., Mángano, M.G., Bahniuk Rumbelsperger, A.M. & Assine, M.L. (2021) Mixed siliciclastic‐carbonate sedimentation in an evolving epicontinental sea: aptian record of marginal marine settings in the interior basins of north‐eastern Brazil. Sedimentology, 68(5), 2125–2164.
     [Google Scholar]
  68. Wang, G. & Carr, T.R. (2012) Methodology of organic‐rich shale lithofacies identification and prediction: a case study from Marcellus Shale in the Appalachian basin. Computers and Geosciences, 49, 151–163.
     [Google Scholar]
  69. Wang, G. & Carr, T.R. (2013) Organic‐rich marcellus shale lithofacies modeling and distribution pattern analysis in the appalachian basin. AAPG Bulletin, 97(12), 2173–2205.
     [Google Scholar]
  70. Wang, G., Carr, T.R., Ju, Y. & Li, C. (2014) Identifying organic‐rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian Basin. Computers and Geosciences, 64, 52–60.
     [Google Scholar]
  71. Welling, M. & Teh, Y.W. (2011) Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th international conference on machine learning. Madison, WI: Omnipress, pp. 681–688.
  72. Winz, J. & Engell, S. (2021) Optimization based sampling for gray‐box modeling using a modified upper confidence bound acquisition function. Computer Aided Chemical Engineering, 50, 953–958.
     [Google Scholar]
  73. Wold, S., Esbensen, K. & Geladi, P. (1987) Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52.
     [Google Scholar]
  74. Xu, Z., Guo, Y. & Saleh, J.H. (2021) Efficient hybrid Bayesian optimization algorithm with adaptive expected improvement acquisition function. Engineering Optimization, 53(10), 1786–1804.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13258
Loading
Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs
Geophysical Prospecting 71, 1835 (2023); https://doi.org/10.1111/1365-2478.13258
/content/journals/10.1111/1365-2478.13258
/content/journals/10.1111/1365-2478.13258
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Bayesian optimization; continuous restricted Boltzmann machine; lacustrine carbonate reservoirs; light gradient boosting machine; lithofacies prediction; supervised classification

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