Oil shale in large basins undergoes multiple evolutionary stages, limiting the applicability of a single logging-based prediction model. This study focuses on the oil shale of the Qingshankou Formation in the Songliao Basin, using gamma ray (GR), deep resistivity (LLD), acoustic travel time (DT), neutron porosity (CNL), density (DEN), and depth data as input features. The XGBoost algorithm is employed to develop predictive models for total organic carbon (TOC) content, free hydrocarbon (S1), pyrolyzable hydrocarbon (S2), and maximum pyrolysis peak temperature (Tmax). TOC predictions are further stratified for low-maturity, mature, and high-maturity oil shale intervals. The results show that S2 achieves the highest prediction accuracy (R2 = 0.91), due to its strong correlation with hydrogen index (HI) driven by thermal evolution. TOC prediction accuracy (R2= 0.75) is influenced by combined changes in porosity and organic matter evolution. Tmax prediction (R2 = 0.74) depends mainly on depth and CNL. S1 correlates weakly with all well logs, yielding the lowest accuracy (R2= 0.29). Shale maturity plays a critical role in determining the reliability of TOC prediction models. Low-maturity oil shale exhibits the best TOC accuracy (R2= 0.83), as wellpreserved organic matter and high porosity correlate closely with LLD, DT, CNL, and DEN. In mature oil shale, retained hydrocarbon and reduced porosity weaken logging signals, lowering accuracy to R2 = 0.63. In high-maturity intervals, hydrocarbon expulsion and porosity rebound improve accuracy (R2 = 0.69). Our approach provides a cost-effective, continuous method for evaluating lacustrine oil shale resources. It is particularly applicable to the evaluation of uncored wells.
1. Wylie, A. S., Jr., Huntoon, J. E. Log-curve amplitude slicing: visualization of log data and depositional trends in the Middle Devonian Traverse Group, Michigan basin, United States. AAPG Bulletin, 2003, 87(4), 581–608.
2. Jin, X. C., Shah, S. N., Roegiers, J.-C., Zhang, B. An integrated petrophysics and geomechanics approach for fracability evaluation in shale reservoirs. SPE Journal, 2015, 20(3), 518–526.
https://doi.org/10.2118/168589-pa
3. Qi, H., Su, J., Hu, X., Ma, A., Dong, Y., Li, A. Study on well logging technology for the comprehensive evaluation of the “seven properties” of shale oil reservoirs – an example of shale oil in the Lucaogou Formation in the Jimsar Sag, Junggar Basin. Frontiers in Earth Science, 2022, 9, 827380.
https://doi.org/10.3389/feart.2021.827380
4. Zhao, J., Ge, X., Fan, Y., Liu, J., Chen, Y., Xing, L. A genetic algorithm-driven support vector machine to discriminate the kerogen type using conventional geophysical logging data. AAPG Bulletin, 2023, 107(11), 1837–1849.
https://doi.org/10.1306/08022320102
5. Zhao, H., Givens, N. B., Curtis, B. Thermal maturity of the Barnett Shale determined from well-log analysis. AAPG Bulletin, 2007, 91(4), 535–549.
https://doi.org/10.1306/10270606060
6. Fu, Q., Horvath, S. C., Potter, E. C., Roberts, F. Log-derived thickness and porosity of the Barnett Shale, Fort Worth basin, Texas: implications for assessment of gas shale resources. AAPG Bulletin, 2015, 99(1), 119–141.
https://doi.org/10.1306/07171413018
7. Chen, X., Chen, L., Jiang, S., Liu, A., Luo, S., Li, H. et al. Evaluation of shale reservoir quality by geophysical logging for Shuijingtuo Formation of lower Cambrian in Yichang Area, Central Yangtze. Journal of Earth Science, 2021, 32(4), 766–777.
http://dx.doi.org/10.1007/s12583-020-1051-1
8. Mathur, N., Raju, S. V., Kulkarni, T. G. Improved identification of pay zones through integration of geochemical and log data: a case study from Upper Assam Basin, India. AAPG Bulletin, 2001, 85(2), 309–323.
https://doi.org/10.1306/8626C7CB-173B-11D7-8645000102C1865D
9. Hu, S., Liu, W., Liu, Y., Liu, K. Acoustic logging response law in shales based on petrophysical model. Petroleum Science, 2022, 19(5), 2120–2130.
https://doi.org/10.1016/j.petsci.2022.03.015
10. Schmoker, J. W., Hester, T. C. Organic carbon in Bakken Formation, United States portion of Williston Basin. AAPG Bulletin, 1983, 67(12), 2165–2174.
https://doi.org/10.1306/AD460931-16F7-11D7-8645000102C1865D
11. Passey, Q. R., Moretti, F. J., Kulla, J. B., Moretti, F. J., Stroud, J. D. A practical model for organic richness from porosity and resistivity logs. AAPG Bulletin, 1990, 74(12), 1777–1794.
https://doi.org/10.1306/0C9B25C9-1710-11D7-8645000102C1865D
12. Bolandi, V., Kadkhodaie, A., Farzi, R. Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: a case study from the Kazhdumi Formation, the Persian Gulf basin, offshore Iran. Journal of Petroleum Science and Engineering, 2017, 151, 224–234.
https://doi.org/10.1016/j.petrol.2017.01.003
13. Alizadeh, B., Maroufi, K., Heidarifard, M. H. Estimating source rock parameters using wireline data: an example from Dezful Embayment, south west of Iran. Journal of Petroleum Science and Engineering, 2018, 167, 857–868.
https://doi.org/10.1016/j.petrol.2017.12.021
14. Wang, H., Wu, W., Chen, T., Dong, X., Wang, G. An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. Journal of Petroleum Science and Engineering, 2019, 176, 664–678.
https://doi.org/10.1016/j.petrol.2019.01.096
15. Barham, A., Ismail, M. S., Hermana, M., Padmanabhan, E., Baashar, Y., Sabir, O. Predicting the maturity and organic richness using artificial neural networks (ANNs): a case study of Montney Formation, NE British Columbia, Canada. Alexandria Engineering Journal, 2021, 60(3), 3253–3264.
https://doi.org/10.1016/j.aej.2021.01.036
16. Deaf, A. S., Omran, A. A., El-Arab, E. S. Z., Maky, A. B. F. Integrated organic geochemical/petrographic and well logging analyses to evaluate the hydrocarbon source rock potential of the Middle Jurassic upper Khatatba Formation in Matruh Basin, northwestern Egypt. Marine and Petroleum Geology, 2022, 140, 105622.
https://doi.org/10.1016/j.marpetgeo.2022.105622
17. Zhao, J., Ge, X., Fan, Y., Liu, J., Chen, Y., Xing, L. A genetic algorithm-driven support vector machine to discriminate the kerogen type using conventional geophysical logging data. AAPG Bulletin, 2023, 107(11), 1837–1849.
https://doi.org/10.1306/08022320102
18. Kadkhodaie, A., Rezaee, M. R. Estimation of vitrinite reflectance from well log data. Journal of Petroleum Science and Engineering, 2017, 148, 94–102.
https://doi.org/10.1016/j.petrol.2016.10.015
19. Ye, Y., Tang, S., Xi, Z. et al. A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data. Journal of Petroleum Science and Engineering, 2022, 208, 109431.
https://doi.org/10.1016/j.petrol.2021.109431
20. Kamali, M. R., Mirshady, A. A. Total organic carbon content determined from well logs using ΔLogR and neuro fuzzy techniques. Journal of Petroleum Science and Engineering, 2004, 45(3–4), 141–148.
https://doi.org/10.1016/j.petrol.2004.08.005
21. Wang, P., Chen, Z., Pang, X., Hu, K., Sun, M., Chen, X. Revised models for determining TOC in shale play: example from Devonian Duvernay Shale, Western Canada Sedimentary Basin. Marine and Petroleum Geology, 2016, 70, 304–319.
https://doi.org/10.1016/j.marpetgeo.2015.11.023
22. Rahmani, O., Khoshnoodkia, M., Kadkhodaie, A., Pour, A. B., Tsegab, H. Geochemical analysis for determining total organic carbon content based on ∆LogR technique in the South Pars Field. Minerals, 2019, 9(12), 735.
https://doi.org/10.3390/min9120735
23. Meyer, B. L., Nederlof, M. H. Identification of source rocks on wireline logs by density/resistivity and sonic transit time/resistivity crossplots. AAPG Bulletin, 1984, 68(2), 121–129.
https://doi.org/10.1306/AD4609E0-16F7-11D7-8645000102C1865D
24. Zhao, P., Mao, Z., Huang, Z., Zhang, C. A new method for estimating total organic carbon content from well logs. AAPG Bulletin, 2016, 100(8), 1311–1327.
https://doi.org/10.1306/02221615104
25. Wang, J., Xu, Y., Sun, P., Liu, Z., Zhang, J., Meng, Q. et al. 2022. Prediction of organic carbon content in oil shale based on logging: a case study in the Songliao Basin, Northeast China. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2022, 8, 44.
http://dx.doi.org/10.1007/s40948-022-00355-9
26. Tang, B., Meng, Q., Liu, Z., Hu, F., Zhang, P., Dang, W. et al. Logging identification of lithology in fine-grained sedimentary rocks based on the FSSA-HKELM model: a case study of the Qingshankou Formation in the Songliao Basin (NE China). Oil Shale, 2024, 41(3), 163–188.
https://doi.org/10.3176/oil.2024.3.02
27. Cracknell, M. J., Reading, A. M. The upside of uncertainty: identification of lithology contact zones from airborne geophysics and satellite data using random forest and support vector machines. Geophysics, 2013, 78(3), 113–126.
28. Mahmoud, A. A. A., Elkatatny, S., Mahmoud, M., Abouelresh, M., Abdulraheem, A., Ali, A. Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network. International Journal of Coal Geology, 2017, 179, 72–80.
https://doi.org/10.1016/j.coal.2017.05.012
29. Johnson, L. M., Rezaee, R., Kadkhodaie, A., Smith, G., Yu, H. Geochemical property modelling of a potential shale reservoir in the Canning Basin (Western Australia), using artificial neural networks and geostatistical tools. Computers & Geosciences, 2018, 120, 73–81.
https://doi.org/10.1016/j.cageo.2018.08.004
30. Chen, T., Guestrin, C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785–794.
https://doi.org/10.1145/2939672.2939785
31. Liu, B., Wang, H., Fu, X., Bai, Y., Bai, L., Jia, M. et al. Lithofacies and depositional setting of a highly prospective lacustrine shale oil succession from the Upper Cretaceous Qingshankou Formation in the Gulong sag, northern Songliao Basin, northeast China. AAPG Bulletin, 2019, 103(2), 405–432.
https://doi.org/10.1306/08031817416
32. Liu, B., Liu, L., Fu, J., Lin, T., He, J., Liu, X. et al. The Songliao Super Basin in northeastern China. AAPG Bulletin, 2023, 107(8), 1257–1297.
https://doi.org/10.1306/02242321181
33. Feng, Z. Q., Jia, C. Z., Xie, X. N., Zhang, S., Feng, Z. H., Cross, T. A. Tectonostratigraphic units and stratigraphic sequences of the nonmarine Songliao basin. Basin Research, 2010, 22(1), 79–95.
https://doi.org/10.1111/j.1365-2117.2009.00445.x
34. Liu, B., Zhao, X., Fu, X., Yuan, B., Bai, L., Zhang, Y. et al. Petrophysical characteristics and log identification of lacustrine shale lithofacies: a case study of the first member of Qingshankou Formation in the Songliao Basin, Northeast China. Interpretation, 2020, 8(3), 45–57.
https://doi.org/10.1190/INT-2019-0254.1
35. Zhang, X., Zou, C., Zhao, J., Li, N. Organic-rich source rock characterization and evaluation of the Cretaceous Qingshankou Formation: results from geophysical logs of the second scientific drilling borehole in the Songliao Basin, NE China. Geosciences Journal, 2018, 23, 119–135.
http://dx.doi.org/10.1007/s12303-018-0013-4
36. Li, C., Yan, W., Wu, H., Tian, H., Zheng, J., Yu, J. et al. Calculation of oil saturation in clay-rich shale reservoirs: a case study of Qing 1 Member of Cretaceous Qingshankou Formation in Gulong Sag, Songliao Basin, NE China. Petroleum Exploration and Development, 2022, 49(6), 1351–1363.
https://doi.org/10.1016/S1876-3804(23)60354-4
37. Liu, B., Sun, J., Zhang, Y., He, J., Fu, X., Yang, L. et al. Reservoir space and enrichment model of shale oil in the first member of Cretaceous Qingshankou Formation in the Changling Sag, southern Songliao Basin, NE China. Petroleum Exploration and Development, 2021, 48(3), 608–624.
https://doi.org/10.1016/S1876-3804(21)60049-6
38. Xu, J., Bechtel, A., Sachsenhofer, R. F., Liu, Z., Gratzer, R., Meng, Q. et al. High resolution geochemical analysis of organic matter accumulation in the Qingshankou Formation, Upper Cretaceous, Songliao Basin (NE China). International Journal of Coal Geology, 2015, 141–142, 23–32.
https://doi.org/10.1016/j.coal.2015.03.003
39. Wu, H., Xu, H., Zhou, H., Jiang, P., Shang, F., Wang, L. Astronomical control on organic matter enrichment of lacustrine mudstones in the first member of the Late Cretaceous Qingshankou Formation, the Songliao Basin, NE China. Journal of Asian Earth Sciences, 2024, 259, 105906.
https://doi.org/10.1016/j.jseaes.2023.105906
40. Fu, X., Meng, Q., Bai, Y., Su, Y., Jin, M., Huo, Z. et al. Quantitative analysis of paleoenvironment of Qingshankou Formation in northern Songliao Basin, Northeastern China. Interpretation, 2022, 10(3), 75–87.
https://doi.org/10.1190/INT-2021-0153.1
41. Sun, Y., Wang, Y., Liao, L., Shi, S., Liu, J. How grain size influences hydrocarbon generation and expulsion of shale based on Rock-Eval pyrolysis and kinetics? Marine and Petroleum Geology, 2023, 155, 106369.
https://doi.org/10.1016/j.marpetgeo.2023.106369
42. Li, C., Pang, X., Huo, Z., Wang, E., Zue, N. A revised method for reconstructing the hydrocarbon generation and expulsion history and evaluating the hydrocarbon resource potential: example from the first member of the Qingshankou Formation in the Northern Songliao Basin, Northeast China. Marine and Petroleum Geology, 2020, 121, 104577.
https://doi.org/10.1016/j.marpetgeo.2020.104577
43. Milliken, K. L., Zhang, T., Chen, J., Ni, Y. Mineral diagenetic control of expulsion efficiency in organic-rich mudrocks, Bakken Formation (Devonian-Mississippian), Williston Basin, North Dakota, USA. Marine and Petroleum Geology, 2021, 127, 104869.
https://doi.org/10.1016/j.marpetgeo.2020.104869
44. Li, X. S., Zhong, H. J., Zhang, K. X., Li, Z., Yu, Y. X., Feng, X. Q. et al. Pore characteristics and pore structure deformation evolution of ductile deformed shales in the Wufeng-Longmaxi Formation, southern China. Marine and Petroleum Geology, 2021, 127, 104992.
https://doi.org/10.1016/j.marpetgeo.2021.104992
45. Mastalerz, M., Drobniak, A., Stankiewicz, A. B. Origin, properties, and implications of solid bitumen in source-rock reservoirs: a review. International Journal of Coal Geology, 2018, 195, 14–36.
https://doi.org/10.1016/j.coal.2018.05.013
46. Wu, S. T., Zhu, R. K., Cui, J. G., Cui, J. W., Bai, B., Zhang, X. X. et al. Characteristics of lacustrine shale porosity evolution, Triassic Chang 7 Member, Ordos Basin, NW China. Petroleum Exploration and Development, 2015, 42(2), 185−195.
https://doi.org/10.1016/S1876-3804(15)30005-7
47. Huang, W. B., Hersi, O. S., Lu, S. F., Deng, S. W. Quantitative modelling of hydrocarbon expulsion and quality grading of tight oil lacustrine source rocks: case study of Qingshankou 1 member, central depression, Southern Songliao Basin, China. Marine and Petroleum Geology, 2017, 84, 34–48.
https://doi.org/10.1016/j.marpetgeo.2017.03.021
48. He, W., Wang, M., Wang, X., Meng, Q., Wu, Y., Lin, T. et al. Pore structure characteristics and affecting factors of shale in the First Member of the Qingshankou Formation in the Gulong Sag, Songliao Basin. ACS Omega, 2022, 7(40), 35755–35773.
http://dx.doi.org/10.1021/acsomega.2c03804
49. Han, Y. J., Horsfield, B., Wirth, R., Mahlstedt, N., Bernard, S. Oil retention and porosity evolution in organic-rich shales. AAPG Bulletin, 2017, 101(6), 807−827.
https://doi.org/10.1306/09221616069
52. Curtis, M. E., Cardott, B. J., Sondergeld, C. H., Rai, C. S. Development of orga-nic porosity in the Woodford Shale with increasing thermal maturity. International Journal of Coal Geology, 2012, 103, 26–31.
https://doi.org/10.1016/j.coal.2012.08.004
51. Zhang, P., Misch, D., Meng, Q., Bechtel, A., Sachsenhofer, R., Liu, Z. et al. Comprehensive thermal maturity assessment in shales: a case study on the upper cretaceous Qingshankou formation (Songliao Basin, NE China). International Journal of Earth Sciences, 2021, 110, 943–962.
http://dx.doi.org/10.1007/s00531-021-02000-4
