Reducing interpretation risks in organic-rich low-velocity mudstone using rock physics modeling in Yinggehai Basin, South China Sea

Abstract

The mudstone in a specific exploration area of the Yinggehai Basin is rich in organic matter and serves as a paradigmatic example of a low-velocity mudstone formation in rock physics. This type of interval exhibits seismic response characteristics similar to those of hydrocarbon reservoirs, which complicates the identification of gas reservoirs and the prediction of gas-bearing zones in this area. In this study, the analysis and modeling of rock physical characteristics within the exploration area are investigated. Based on the actual drilling curve and coring analysis data, a rock physics model of the low-velocity mudstone reservoir is established using self-consistent approximations and the Ciz–Gassmann model, and the influence of organic matter content on the mudstone’s elastic properties is analyzed. Additionally, a robust quantitative inversion procedure is introduced to test the feasibility of inverting for porosity, clay content, and water saturation to mitigate the risk of low-velocity mudstone. The application of actual data, including core samples, logging data, and seismic database, demonstrates the effectiveness of this method and provides technical support for seismic prediction of sand body identification and hydrocarbon detection.

Keywords

Low-velocity mudstone / Rock physics / Hydrocarbon detection / Total organic carbon / Seismic interpretation

Funding

This study was financially supported by the National Oil and Gas Major Project of China, “Amplitude preserving imaging technology with high precision and multiple geophysical field exploration technology for deep-water complex geological conditions in South China Sea” (Grant No. 2025ZD1402706).

Conflict of interest

The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Author contributions

Conceptualization: Jinpeng Li, Yanhua Xie; Formal analysis: Yanhua Xie, Jinpeng Li, Wanyuan Sun; Investigation: Yanhua Xie, Wanyuan Sun, Yayun Dang; Methodology: Jinpeng Li, Yanhua Xie, Fang Li; Visualization: Yanhua Xie, Wanyuan Sun; Writing–original draft: Yanhua Xie, Wanyuan Sun; Writing–review & editing: Yanhua Xie, Jinpeng Li, Yayun Dang

Availability of data

The data in this paper is subject to the following licenses/restrictions: National confidential data. Requests to access these datasets should be directed to the corresponding author.

References

[1] Guo XX , Hu L , Wu YY , Xiong XF , Yu HW , Xia L. Gas generation potential of Oligocene source rocks in Yinggehai Basin. Nat Gas Ind B. 2024; 11(6): 728-738. doi: 10.1016/j.ngib.2024.11.006

[2] Pei JX , Yu JF , Wang LF , Hao DF , Liu F. Key challenges and strategies for the success of natural gas exploration in mid—deep strata of the Yinggehai Basin. Acta Pet Sin . 2011; 32(4): 573-579. [in Chinese]. doi: 10.7623/syxb201104003

[3] Wu KQ , Pei JX , Hu L , Hu GW , Tang SM , Zhao H. Accumulation model and exploration direction of medium—large gas fields in Yinggehai Basin. Acta Pet Sin. 2023; 44(12): 2200-2216. [in Chinese]. doi: 10.7623/syxb202312012

[4] Zhang XY , Fan CW , Alves TM , et al. Hydrocarbon generation in source rocks with low organic matter as the primary mechanism for overpressure development: A case study from the Yinggehai Basin, South China Sea. J Geol Soc. 2025; 183(1): jgs2024—157. doi: 10.1144/jgs2024—157

[5] Xie YH , Li XS , Tong CX , Liu P , Wu HZ , Huang ZL. High temperature and high pressure gas enrichment condition, distribution law and accumulation model in central diaper zone of Yinggehai basin. China Offshore Oil Gas. 2015; 27(4): 1-12. [in Chinese]. doi: 10.11935/j.issn.1673-1506.2015.04.001

[6] Yang GX , Yin HW , Jia D , Wang HB , Wang W , Xu WQ. New insights into the structure of the Yinggehai Basin and its tectonic implications, South China Sea: Evidence from scaled physical models. Basin Res. 2024; 36(4): e12888. doi: 10.1111/bre.12888

[7] Xie YH , Gao YD. Recent domestic exploration progress and direction of CNOOC. China Pet Explor. 2020; 25(1): 20-30. [in Chinese]. doi: 10.3969/j.issn.1672—7703.2020.01.003

[8] Xie YH. China National Offshore Oil Corporation: Major achievements in oil and gas exploration of CNOOC in the 13th Five—Year Plan period and prospects in the 14th Five—Year Plan period. China Pet Explor. 2021; 26(1): 43-54. [in Chinese]. doi: 10.3969/j.issn.1672—7703.2021.01.004

[9] Tong CX , Wang ZF , Li XS. Pooling conditions of gas reservoirs in the Dongfang 1—1 Gas Field, Yinggehai Basin. Nat Gas Ind. 2012; 32(8): 11-15. [in Chinese]. doi: 10.3787/j.issn.1000—0976.2012.08.003

[10] Li F , Deng Y , Hu L , Zhou F. Porosity prediction by geophysics technology at low—porosity and low—permeability reservoir of the Yinggehai Basin. Bull Geol Sci Technol. 2022; 41(4): 84-90. [in Chinese]. doi: 10.19509/j.cnki.dzkq.2021.0060

[11] Grana D , Rossa ED , Della Rossa E. Probabilistic petrophysical—properties estimation integrating statistical rock physics with seismic inversion. Geophysics. 2010; 75(3): O21-O37. doi: 10.1190/1.3386676

[12] Russell BH , Gray D , Hampson DP . Linearized AVO and poroelasticity. Geophysics . 2011; 76(3): C19-C29. doi: 10.1190/1.3555082

[13] Yin XY , Cui W , Zong ZY , Liu XJ. Petrophysical property inversion of reservoirs based on elastic impedance. Chin J Geophys. 2014; 57(12): 4132-4140. [in Chinese]. doi: 10.6038/cjg20141224

[14] Zong ZY , Yin XY , Wu GC. Fluid identification method based on compressional and shear modulus direct inversion. Chin J Geophys. 2012; 55(1): 284-292. [in Chinese]. doi: 10.6038/j.issn.0001—5733.2012.01.028

[15] Luo C , Ba J , Guo Q. Probabilistic seismic petrophysical inversion with statistical double—porosity Biot—Rayleigh model. Geophysics. 2023; 88(3): M157-M171. doi: 10.1190/geo2022—0288.1

[16] Li K , Yin X , Zong Z. Facies—constrained prestack seismic probabilistic inversion driven by rock physics. Sci China Earth Sci. 2020; 63(6): 822-840. doi: 10.1007/s11430—019—9578—1

[17] Ba J , Ai ZJ , Carcione JM , Pang MQ , Yan XF , Chen X. Estimation of pore structure for heterogeneous reservoirs based on the theory of differential poroelasticity. Pure Appl Geophys. 2024; 181(7): 2131-2147. doi: 10.1007/s00024—024—03510—3

[18] Li H , Zhang J , Pan H , Gao Q. Nonlinear simultaneous inversion of pore structure and physical parameters based on elastic impedance. Sci China Earth Sci. 2021; 64(6): 977-991. doi: 10.1007/s11430—020—9773—8

[19] Carcione JM. A model for seismic velocity and attenuation in petroleum source rocks. Geophysics . 2000; 65(4): 1080-1092. doi: 10.1190/1.1444801

[20] Zhang GZ , Chen JJ , Chen HZ , Ma ZG , Li CC , Yin XY. Prediction for in—situ formation stress of shale based on rock physics equivalent model. Chin J Geophys. 2015; 58(6): 2112-2122. [in Chinese]. doi: 10.6038/cjg20150625

[21] Ba J , Xu W , Fu LY , Carcione JM , Zhang L. Rock anelasticity due to patchy—saturation and fabric heterogeneity: A double—double porosity model of wave propagation. J Geophys Res Solid Earth. 2017; 122(3): 1949-1976. doi: 10.1002/2016JB013882

[22] Dutilleul J , Bourlange S , Géraud Y , Stemmelen D. Porosity, pore structure, and fluid distribution in the sediments entering the northern Hikurangi margin, New Zealand. J Geophys Res Solid Earth. 2020; 125(11): 1-21. doi: 10.1029/2020JB020330

[23] Hill R. The elastic behavior of crystalline aggregate. Proc Phys Soc . 1952; 65(5): 349-354. doi: 10.1088/0370—1298/65/5/307

[24] Berryman JG. Long—wavelength propagation in composite elastic media I. Spherical inclusions. J Acoust Soc Am. 1980; 68(6): 1809-1819. doi: 10.1121/1.385171

[25] Berryman JG. Long—wavelength propagation in composite elastic media II. Ellipsoidal inclusions. J Acoust Soc Am. 1980; 68(6): 1820-1831. doi: 10.1121/1.385172

[26] Ciz R , Shapiro SA. Generalization of Gassmann’s equations for porous media saturated with a solid material. Geophysics. 2007; 72(6): A75-A79. doi: 10.1190/1.2772400

[27] Brie A , Pampuri F , Marsala AF , Meazza O. Shear sonic interpretation in gas—bearing sands. In: SPE Annual Technical Conference and Exhibition. SPE Annual Technical Conference and Exhibition; October 22—25, 1995; Dallas, Texas. Society of Petroleum Engineers (SPE); 1995. doi: 10.2118/30595—ms

[28] Gassmann F. Elastic waves through a packing of spheres. Geophysics. 1951; 16(4): 673-685. doi: 10.1190/1.1437718

[29] Yu SH , Zong ZY , Li K. Rock physics and seismic reflectivity parameterization for amplitude—variation—with—angle inversion of low—maturity source rocks. Geophysics. 2023; 88(6): R703-R719. doi: 10.1190/geo2022—0734.1

[30] Zong ZY, Yu SH, Tan H , Yin XY, Li K. Nonlinear seismic inversion of total organic carbon for hydrocarbon source rocks. Geophysics . 2025; 90(5): MR375-MR390. doi: 10.1190/geo2024—0288.1

[31] Zhao LX , Qin X , Han DH , Geng JH , Yang ZF , Cao H. Rock—physics modeling for the elastic properties of organic shale at different maturity stages. Geophysics. 2016; 81(5): D527-D541. doi: 10.1190/geo2015—0713.1

[32] Zhang JJ , Li HB , Yao FC. Rock critical porosity inversion and S—wave velocity prediction. Appl Geophys. 2012; 9(1): 57-64. doi: 10.1007/s11770—012—0314—8

[33] Yang YH , Yin XY , Gao G , Gui ZX , Zhao B. Shear—wave velocity estimation for calciferous sandy shale formation. J Geophys Eng. 2019; 16(1): 105-115. doi: 10.1093/jge/gxy009

[34] Wang JL , Wu SG , Zhao LX , Wang WW , Wei JG , Sun J. An effective method for shear—wave velocity prediction in sandstones. Mar Geophys Res. 2019; 40(4): 655-664. doi: 10.1007/s11001—019—09396—4

[35] Zhang L , Ba J , Carcione JM , Wu CF. Seismic wave propagation in partially saturated rocks with a fractal distribution of fluid—patch size. J Geophys Res Solid Earth. 2022; 127(2): e2021JB023809. doi: 10.1029/2021JB023809

[36] Pang MQ , Ba J , Carcione JM , Picotti S , Zhou J , Jiang R. Estimation of porosity and fluid saturation in carbonates from rock—physics templates based on seismic Q. Geophysics. 2019; 84(6): M25-M36. doi: 10.1190/geo2019—0031.1

[37] Yin XY , Zong ZY , Wu GC. Research on seismic fluid identification driven by rock physics. Sci China Earth Sci. 2015; 58(2): 159-171. doi: 10.1007/s11430—014—4992—3

[38] Chapman S , Tisato N , Quintal B , Holliger K. Seismic attenuation in partially saturated Berea sandstone submitted to a range of confining pressures. J Geophys Res Solid Earth. 2016; 121(3): 1664-1676. doi: 10.1002/2015JB012575

[39] Bosch M , Mukerji T , Gonzalez EF. Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A Review. Geophysics. 2010; 75(5): A165-A176. doi: 10.1190/1.3478209

[40] Zhu DH , Gibson R. Seismic inversion and uncertainty quantification using transdimensional Markov Chain Monte Carlo method. Geophysics. 2018; 83(4): R321-R334. doi: 10.1190/geo2016—0594.1

[41] Kolbjørnsen O , Arild B , Ragnar H , Per R , Abel Onana N , Eyvind A. Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction. Geophysics. 2020; 85(3): R207-R21. doi: 10.1190/geo2019—0170.1

[42] Wang C , Yin C , Shi XW , et al. Direct prediction method of fracturing ability in shale formations based on pre—stack seismic inversion. J Seism Explor. 2022; 31(5): 407-424.

[43] Zhou XY , Gan LD , Yang TQ , Jiang XY , Wei LL. S—wave velocity estimation and reservoir type identification based on partially connected porosity model of carbonate reservoir. J Seism Explor. 2023; 32(1): 51-65.

[44] Sharifi J , Amiri M , Fakhar M , Hafezi Moghaddas N. An experimental study on variation of pore pressure and wave velocity in carbonate rocks: impact of depositional facies and diagenesis. Geomech Geophys Geo—Energy Geo—Resour. 2026; 12(8): 1-31. doi: 10.1007/s40948—025—01062—x