Microscopic heterogeneity characteristics and movable oil characterization of ultra-deep shale oil in the Mahu Sag

Abstract

The shale oil in the Fengcheng Formation of the Mahu Sag has become an extremely critical target for large-scale reserve growth in the Junggar Basin. Characterized by great burial depth, complex geology, and pronounced heterogeneity, the effective and economical development of shale oil “sweet spots” remains a key challenge. To clarify the characteristics and controlling factors of this heterogeneity, an integrated analysis combining geological context, well-log responses, and laboratory data was conducted, focusing on three aspects: litho-mineralogical composition, pore structure, and hydrocarbon content. This approach enabled a detailed characterization of the micro-scale heterogeneity of the Fengcheng Formation shale oil and identification of its primary controls. On this basis, a four-parameter coupled mobility index evaluation model was developed, and-by integrating formation test data and fracture intensity-a classification scheme for mobility ranges was established. The study reveals that the strong heterogeneity is intrinsically driven by the complex coupling among lithofacies assemblages, clay mineral distribution, pore structure, and oil saturation, while depositional environment and diagenesis act as the dominant macro-scale controlling factors. Field production data confirm that the proposed mobility evaluation method correlates well with actual performance: the mobility index exhibits a clear exponential relationship with average daily oil production ( R² = 0.90), and the mobility-based classification shows excellent agreement with well productivity, thereby providing a quantitative foundation for the effective development of ultra-deep shale oil sweet spots.

Keywords

Junggar basin / Mahu sag / Ultra-deep shale oil / Heterogeneity characteristics / Mobility characterization

References

[1] S. Mi, Q. Guo, Q. Zhang, et al., Classification and potential of continental shale oil resources in China and resource evaluation methods and criteria, Oil Shale 40 (4) (2023) 283-320, https://doi.org/10.3176/oil.2023.4.02.

[2] T. Hu, F. Jiang, X. Pang, et al., Identification and evaluation of shale oil micromigration and its petroleum geological significance, Petrol. Explor. Dev. 51 (1) (2024) 127-140, https://doi.org/10.1016/S1876-3804(24)60010-8.

[3] L. Wang, B. Liu, L. Bai, et al., Pore evolution modeling in natural lacustrine shale influenced by mineral composition: implications for shale oil exploration and CO₂ storage , Adv. Geo-Energy Res. 13 (3) (2024) 218-230, https://doi.org/10.46690/ager.2024.09.07.

[4] W. Guo, S. Deng, Y. Sun, Recent advances on shale oil and gas exploration and development technologies, Adv. Geo-Energy Res. 11 (2) (2024) 81-87, https://doi.org/10.46690/ager.2024.02.01.

[5] J. Lv, T. Hu, W. Zhang, et al., Microscopic oil occurrence in the Permian alkaline lacustrine shales: fengcheng formation, mahu Sag, Junggar basin, Pet. Sci. 22 (4) (2025) 1407-1427, https://doi.org/10.1016/j.petsci.2025.01.005.

[6] Y Tang, C Jia, F Chen, et al., Relationship between pore throat structure and crude oil mobility of full particle sequence reservoirs in Permian Fengcheng Formation, Mahu Sag, Junggar Basin, NW China, Petrol. Explor. Dev. 52 (1) 112-124, https://doi.org//10.1016/S1876-3804(25)60008-5.

[7] F. Jiang, M. Hu, T. Hu, et al., Controlling factors and models of shale oil enrichment in Lower Permian Fengcheng Formation, Mahu Sag, Junggar Basin, NW China, Petrol. Explor. Dev. 50 (4) (2023) 812-825, https://doi.org/10.1016/S1876-3804(25)60008-5.

[8] B. Xiang, W. Ma, D. He, et al., Heterogeneity of alkaline lacustrine source rocks of the lower Permian Fengcheng Formation in the Mahu Sag, Junggar Basin, northwestern China, Energy Explor. Exploit. 41 (2) (2023) 370-390, https://doi.org/10.1177/01445987221117838.

[9] Y. Tang, Z. Lyu, W. He, et al., Origin of dolomites in the Permian dolomitic reservoirs of Fengcheng Formation in Mahu Sag, Junggar Basin, NW China, Petrol. Explor. Dev. 50 (1) (2023) 43-56, https://doi.org/10.1016/S1876-3804(22)60368-9.

[10] Z. Yu, D. Yan, C. Adenutsi, Geologically constrained deep learning for lithofacies identification of mixed terrestrial shale reservoirs: permian fengcheng Formation, Mahu Sag, Junggar Basin, Western China, SPE J. 30 (5) (2025) 2653-2672, https://doi.org/10.2118/225443-PA.

[11] Z. Wang, Z. Gao, W. He, et al., Using spontaneous imbibition to evaluate the hydrocarbon migration and accumulation potential of shale reservoirs: a case Study of the Permian fengcheng Formation in the Mahu Sag, Junggar basin, Energy Fuel. 37 (1) (2023) 360-372, https://doi.org/10.1021/acs.energyfuels.2c03897.

[12] Y. Zhou, Z. Xiao, Y. Li, et al., Lithofacies assemblages of shale and dense shell limestone: implications for shale oil exploration, Front. Earth Sci. (2025) 1-19, https://doi.org/10.1007/s11707-024-1122-1.

[13] R. Xu, T. Yan, S. Sun, et al., Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder, Oil Shale 42 (1) (2025) 79-114, https://doi.org/10.3176/oil.2025.1.04.

[14] Q. Zhang, W. Wang, Y. Su, et al., A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics, Pet. Sci. 21 (1) (2024) 327-342, https://doi.org/10.1016/j.petsci.2023.10.003.

[15] R. Guo, G. Yu, K. Wen, et al., Pore characteristics and its impact on shale oil occurrence of lacustrine shale from Chang 7, Ordos Basin, Petrol. Sci. Technol. 43 (18) (2025) 2501-2520, https://doi.org/10.1080/10916466.2024.2391481.

[16] M. Zamiri, J. Guo, F. Marica, et al., Shale characterization using T1-T2*Magnetic resonance relaxation correlation measurement at low and high magnetic fields, Petrophysics 64 (3) (2023) 384-401, https://doi.org/10.30632/PJV64N3-2023a5.

[17] X. Chang, X. Wang, C. Yang, et al., Vertical height growth mechanism of hydraulic fractures in laminated shale oil reservoirs based on 3D discrete lattice modeling, J. Pet. Explor. Prod. Technol. 14 (3) (2024) 785-804, https://doi.org/10.30632/PJV64N3-2023a5.

[18] S. Dash, A. Garcia, Z. Heidari, A new workflow for improved Resistivity-based water saturation assessment in Organic-rich mudrocks: application to Haynesville, Eagle ford, and woodford formations, SPE Reservoir Eval. Eng 26 (3) (2023) 527-542, https://doi.org/10.2118/214656-PA.

[19] K. Valerii, K. Vladimir, K Yury Kovalenko, et al., Changes in filtration and capacitance properties of highly porous reservoir in underground gas storage: CT-based and geomechanical modeling, J. Rock Mech. Geotech. Eng. 16 (8) (2024) 2982-2995, https://doi.org/10.1016/j.jrmge.2023.12.015.

[20] Khawaja Hasnain Iltaf, Q. Hu, M. Fan, et al., Multiscale pore characterization of the new Albany shale: insights from complementary analytical techniques, Energy Fuel. 39 (22) (2025) 10356-10373, https://doi.org/10.1021/acs.energyfuels.5c00862.

[21] B.R. Phanikumar, S. Dembla, A. Yatindra, Swelling behaviour of an expansive clay blended with fine sand and fly ash, Geotech. Geol. Eng. 39 (1) (2021) 583-591, https://doi.org/10.1007/s10706-020-01480-6.

[22] W. Yang, C. Zhang, X. Shi, et al., Reconstruction of LWD-NMR T2 water spectrum and fluid recognition based on microscopic pore structure constraints, Geoenergy Sci. Eng. 221 (2023) 211386, https://doi.org/10.1016/j.geoen.2022.211386.

[23] W. Wang, Z. Wang, C. Liu, et al., Key technologies for shale oil sweet spot evaluation and classification of sweet spot types: a case study from the Permian fengcheng formation in the mahu sag, Earth Sci. 48 (1) (2023) 223-234, https://doi.org/10.3799/dqkx.2022.333.

[24] J. Guo, Z. Zhang, X. Nie, et al., Logging evaluation of irreducible water saturation: fractal theory and data-driven approach-Case study of complex porous carbonate reservoirs in mishrif formation, Fractals 8 (8) (2024) 487, https://doi.org/10.3390/fractalfract8080487.

[25] W. Xie, Q. Yin, J. Zeng, et al., Fractal-based approaches to pore structure investigation and water saturation prediction from NMR measurements: a case study of the gas-bearing tight sandstone reservoir in nanpu sag, Fractals 7 (3) (2023) 273, https://doi.org/10.3390/fractalfract7030273.

[26] J. Zhang, Pore pressure prediction from well logs: methods, modifications, and new approaches, Earth Sci. Rev. 108 (1-2) (2011) 50-63, https://doi.org/10.1016/j.earscirev.2011.06.001.

[27] B.A. Eaton, The effect of overburden stress on geopressure prediction from well logs, J. Petrol. Technol. 24 (8) (1972) 929-934, https://doi.org/10.2118/3719-PA.