The lithology distribution of fine-grained sedimentary rocks in regional space plays a crucial role in guiding the exploration of oil shale layers. Identifying lithology by conventional logging information is efficient and cost-effective. However, the strong inhomogeneity of fine-grained sedimentary rocks leads to a complex nonlinear relationship between lithology and logging data, making conventional linear methods no longer applicable. This study proposes a hybrid model for logging lithology identification of fine-grained sedimentary rocks from the first member of Qingshankou Formation in the Songliao Basin, NE China. This model is based on the hybrid kernel extreme learning machine (HKELM), and the firefly perturbation strategy is introduced into the sparrow search algorithm (FSSA) for optimization. The lithologic distribution is determined using cores, thin sections, and total organic carbon (TOC), while a total of four logging curves, the acoustic (AC), density (DEN), resistivity (RT), and natural gamma (GR) curves, were collected. FSSA-HKELM was compared with five algorithms, ELM, KELM, HKELM, PSO-HKELM, and SSA-HKELM, for lithology prediction effectiveness. The proposed hybrid method outperformed the other algorithms, achieving an accuracy of 81.78%, precision of 81.65%, recall of 87.78%, and a weighted F1 score of 82.16%. FSSA-HKELM is a very effective lithological identification method, which provides a basis for the lithological prediction of oil shale-bearing formations.
1. Aplin, A. C., Macquaker, J. H. S. Mudstone diversity: origin and implications for source, seal, and reservoir properties in petroleum systems. AAPG Bull., 2011, 95(12), 2031–2059.
https://doi.org/10.1306/03281110162
2. Jiang, Z. X., Liang, C., Wu, J., Zhang, J. G., Zhang, W. Z., Wang, Y. S., Liu, H. M., Chen, X. Several issues in sedimentological studies on hydrocarbon-bearing fine-grained sedimentary rocks. Acta Pet. Sin., 2013, 34(6), 1031–1039.
https://doi.org/10.7623/syxb201306001
3. Zou, C. N., Zhu, R. K., Chen, Z. Q., Ogg, J. G., Wu, S. T., Dong, D. Z., Qiu, Z., Wang, Y. M., Wang, L., Lin, S. H., Cui, J. W., Su, L., Yang, Z. Organic-matter-rich shales of China. Earth-Sci. Rev., 2019, 189, 51–78.
https://doi.org/10.1016/j.earscirev.2018.12.002
4. He, J. H., Ding, W. L., Jiang, Z. X., Li, A., Wang, R. Y., Sun, Y. X. Logging identification and characteristic analysis of the lacustrine organic-rich shale lithofacies: a case study from the Es3L shale in the Jiyang Depression, Bohai Bay Basin, Eastern China. J. Pet. Sci. Eng., 2016, 145, 238–255.
https://doi.org/10.1016/j.petrol.2016.05.017
5. Bartetzko, A., Delius, H., Pechnig, R. Effect of compositional and structural variations on log responses of igneous and metamorphic rocks. I: mafic rocks. Geol. Soc. Spec. Publ., 2005, 240, 255–278.
https://doi.org/10.1144/gsl.sp.2005.240.01.19
6. Zhao, X. Z., Pu, X. G., Han, W. Z., Zhou, L. H., Shi, Z. N., Chen, S. Y., Xiao D. Q. A new method for lithology identification of fine grained deposits and reservoir sweet spot analysis: a case study of Kong 2 Member in Cangdong sag, Bohai Bay Basin, China. Pet. Explor. Dev., 2017, 44(4), 524–534.
https://doi.org/10.1016/S1876-3804(17)30061-7
7. Gregori, E. B., Zhang, J. J., Galván-Fernández, C., Fernández-Navarro, F. Learner support in MOOCs: identifying variables linked to completion. Comput. Educ., 2018, 122, 153–168.
https://doi.org/10.1016/j.compedu.2018.03.014
8. Tran, V. L., Nguyen, D.-D. Novel hybrid WOA-GBM model for patch loading resistance prediction of longitudinally stiffened steel plate girders. Thin-Walled Struct., 2022, 177, 109424.
https://doi.org/10.1016/j.tws.2022.109424
9. Wang, X., Gao, S., Guo, Y. B., Zhou, S. Y., Duan, Y. H., Wu, D. Q. A combined prediction model for hog futures prices based on WOA-LightGBM-CEEMDAN. Complexity, 2022.
https://doi.org/10.1155/2022/3216036
10. Dev, V. A., Eden, M. R. Formation lithology classification using scalable gradient boosted decision trees. Comput. Chem. Eng., 2019, 128, 392–404.
https://doi.org/10.1016/j.compchemeng.2019.06.001
11. Xie, Y. X., Zhu, C. Y., Zhou, W., Li, Z. D., Liu, X., Tu, M. Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances. J. Pet. Sci. Eng., 2018, 160, 182–193.
https://doi.org/10.1016/j.petrol.2017.10.028
12. Ye, Z. H., Guo, S. Y., Chen, D., Wang, H., Li, S. D. Drilling formation perception by supervised learning: model evaluation and parameter analysis. J. Nat. Gas Sci. Eng., 2021, 90, 103923.
https://doi.org/10.1016/j.jngse.2021.103923
13. Zhang, J. L., He, Y. B., Zhang, Y., Li, W. F., Zhang, J. J. Well-logging-based lithology classification using machine learning methods for high-quality reservoir identification: a case study of Baikouquan Formation in Mahu area of Junggar Basin, NW China. Energies, 2022, 15(10), 3675.
https://doi.org/10.3390/en15103675
14. Jadoon, Q. K., Roberts, E. M., Henderson, B., Blenkinsop, T. G., Wüst, R. A. J., Mtelela, C. Lithological and facies analysis of the Roseneath and Murteree shales, Cooper Basin, Australia. J. Nat. Gas Sci. Eng., 2016, 37, 138–168.
https://doi.org/10.1016/j.jngse.2016.10.047
15. Woo, J., Rhee, C. W., Jeoung, T. J., Jang, S. Application of multi-resolution graph-based clustering for electrofacies prediction: a case study from the Horn River Shale, British Columbia, Canada. Geosci. J., 2020, 24(5), 507–518.
https://doi.org/10.1007/s12303-019-0046-3
16. Huang, G. B., Zhu, Q. Y., Siew, C. K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70(1–3), 489–501.
https://doi.org/10.1016/j.neucom.2005.12.126
17. Huang, G. B., Zhou, H. M., Ding, X. J., Zhang, R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern., 2012, 42(2), 513–529.
https://doi.org/10.1109/TSMCB.2011.2168604
18. Wang, Z. G., Chen, H. Y., Wang, M., Zhang, X., Dou, Y. H. Solid particle erosion prediction in elbows based on machine learning and swarm intelligence algorithm. J. Pet. Sci. Eng., 2022, 218, 111042.
https://doi.org/10.1016/j.petrol.2022.111042
19. Yan, S. C., Wu, L. F., Fan, J. L., Zhang, F. C., Zou, Y. F., Wu, Y. A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: applications in arid and humid regions of China. Agric. Water Manag., 2021, 244(1), 106594.
https://doi.org/10.1016/j.agwat.2020.106594
20. Yu, C. X., Koopialipoor, M., Murlidhar, B. R., Mohammed, A. S., Armaghani, D. J., Mohamad, E. T., Wang, Z. L. Optimal ELM–Harris Hawks optimization and ELM–Grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Nat. Resour. Res., 2021, 30(4), 2647–2662.
https://doi.org/10.1007/s11053-021-09826-4
21. Sun, Z. X., Jiang, B. S., Li, X. L., Li, J. K., Xiao, K. A data-driven approach for lithology identification based on parameter-optimized ensemble learning. Energies, 2020, 13(15), 3903.
https://doi.org/10.3390/en13153903
22. Gu, Y. F., Zhang, D. Y., Lin, Y. B., Ruan, J. F., Bao, Z. D. Data-driven lithology prediction for tight sandstone reservoirs based on new ensemble learning of conventional logs: a demonstration of a Yanchang member, Ordos Basin. J. Pet. Sci. Eng., 2021, 207, 109292.
https://doi.org/10.1016/j.petrol.2021.109292
23. Tang, J., Liu, G., Pan, Q. T. A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE-CAA J. Autom. Sin., 2021, 8(10), 1627–1643.
https://doi.org/10.1109/JAS.2021.1004129
24. Dorigo, M., Maniezzo, V., Colorni, A. Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern., 1991, 26(1), 29–41.
https://doi.org/10.1109/3477.484436
25. Kennedy, J., Eberhart, R. Particle swarm optimization. In: Proceedings of ICNN’95 – International Conference on Neural Networks, November 27–December 1, 1995, Perth, Australia. IEEE, 2017, 1942–1948.
https://doi.org/10.1109/ICNN.1995.488968
26. Mirjalili, S., Mirjalili, S. M., Lewis, A. Grey wolf optimizer. Adv. Eng. Softw., 2014, 69, 46–61.
https://doi.org/10.1016/j.advengsoft.2013.12.007
27. Mirjalili, S., Lewis, A. The whale optimization algorithm. Adv. Eng. Softw., 2016, 95, 51–67.
https://doi.org/10.1016/j.advengsoft.2016.01.008
28. Xue, J. K., Shen, B. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng., 2020, 8(1), 22–34.
https://doi.org/10.1080/21642583.2019.1708830
29. Nguyen, T. T., Ngo, T. G., Dao, T. K., Nguyen, T. T. T. Microgrid operations planning based on improving the flying sparrow search algorithm. Symmetry, 2022, 14(1), 168.
https://doi.org/10.3390/sym14010168
30. Qiao, L., Jia, Z. N., Cui, Y., Xiao, K., Su, H. N. Shear sonic prediction based on DELM optimized by improved sparrow search algorithm. Appl. Sci., 2022, 12(16), 8260.
https://doi.org/10.3390/app12168260
31. Feng, Z. Q., Jia, C. Z., Xie, X. N., Zhang, S., Feng, Z. H., Cross, T. A. Tectono-stratigraphic units and stratigraphic sequences of the nonmarine Songliao basin. Basin Res., 2010, 22(1), 79–95.
https://doi.org/10.1111/j.1365-2117.2009.00445.x
32. Xu, J. J., Bechtel, A., Sachsenhofer, R. F., Liu, Z. J., Gratzer, R., Meng, Q. T., Song, Y. High resolution geochemical analysis of organic matter accumulation in the Qingshankou Formation, Upper Cretaceous, Songliao Basin (NE China). Int. J. Coal Geol., 2015, 141–142, 23–32.
https://doi.org/10.1016/j.coal.2015.03.003
33.Gao, R. Q., Cai, X. Y. Formation Conditions and the Distribution of Oil and Gas Fields in Songliao Basin. Petroleum Industry Press, Beijing, 1997.
34. Chen, C. Non-marine setting of petroleum in the Sungliao basin of Northeast China. J. Pet. Geol., 1980, 2(3), 233–264.
https://doi.org/10.1111/j.1747-5457.1980.tb00706.x
35. Bechtel, A., Jia, J. L., Strobl, S. A. I., Sachsenhofer, R. F., Liu, Z. J., Gratzer, R., Püttmann, W. Palaeoenvironmental conditions during deposition of the Upper Cretaceous oil shale sequences in the Songliao Basin (NE China): implications from geochemical analysis. Org. Geochem., 2012, 46, 76–95.
https://doi.org/10.1016/j.orggeochem.2012.02.003
36. Jia, J. L., Bechtel, A., Liu, Z. J., Strobl, S. A. I., Sun, P. C., Sachsenhofer, R. F. Oil shale formation in the Upper Cretaceous Nenjiang Formation of the Songliao Basin (NE China): implications from organic and inorganic geochemical analyses. Int. J. Coal Geol., 2013, 113, 11–26.
https://doi.org/10.1016/j.coal.2013.03.004
37. Jackson, L. L., Roof, S. R. Determination of the forms of carbon in geologic materials. Geostand. Geoanalytical Res., 1992, 16(2), 317–323.
https://doi.org/10.1111/j.1751-908X.1992.tb00495.x
38. Ding, S. F., Zhao, H., Zhang, Y. N., Xu, X. Z., Nie, R. Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev., 2015, 44(1), 103–115.
https://doi.org/10.1007/s10462-013-9405-z
39. Tian, Z. D., Li, S. J., Wang, Y. H., Wang, X. D. Wind power prediction method based on hybrid kernel function support vector machine. Wind Eng., 2018, 42(3), 252–264.
https://doi.org/10.1177/0309524X17737337
40. Ahuja, B., Vishwakarma, V. P. Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification. Multimed. Tools Appl., 2021, 80(21), 32423–32447.
https://doi.org/10.1007/s11042-021-11097-3
41. Lv, L., Wang, W. H., Zhang, Z. Y., Liu, X. G. A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl.-Based Syst., 2020, 195, 105648.
https://doi.org/10.1016/j.knosys.2020.105648
42. Gandomi, A. H., Yang, X. S., Talatahari, S., Alavi, A. H. Firefly algorithm with chaos. Commun. Nonlinear Sci., 2013, 18(1), 89–98.
https://doi.org/10.1016/j.cnsns.2012.06.009
43. Hutton, A. C. Petrographic classification of oil shales. Int. J. Coal Geol., 1987, 8(3), 203–231.
https://doi.org/10.1016/0166-5162(87)90032-2
44. Liu, Z. J., Yang, H. L., Dong, Q. S., Zhu, J. W., Guo, W., Ye, Q. S., Liu, R., Meng, Q. T., Zhang, H. L., Gan, S. C. Oil Shale in China. Petroleum Industry Press, Beijing, 2009.
45. Zhao, W. Z., Hu, S. Y., Hou, L. H. Connotation and strategic role of in-situ conversion processing of shale oil underground in the onshore China. Pet. Explor. Dev., 2018, 45(4), 563–572.
https://doi.org/10.1016/S1876-3804(18)30063-6
46. Passey, Q. R., Creaney, S., Kulla, J. B., Moretti, F. J., Stroud, J. D. A practical model for organic richness from porosity and resistivity logs. AAPG Bull., 1990, 74(12), 1777–1794.
https://doi.org/10.1306/0C9B25C9-1710-11D7-8645000102C1865D
47. Hu, H. T., Lu, S. F., Liu, C., Wang, W. M., Wang, M., Li, J. J., Sang, J. H. Models for calculating organic carbon content from logging information: comparison and analysis. Acta Sedimentol. Sin., 2011, 29(6), 1199–1205.
https://doi.org/10.14027/j.cnki.cjxb.2011.06.012
