AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025460108
Submit to JSE
Cite this article
20
Download
45
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Advances in intelligent seismic interpretation for onshore unconventional reservoirs in CNOOC: The Linxing–Shenfu gas field case

Wenlan Li1 ,  Qixin Li2 ,  Xiaowen Zheng3 ,  Bo Wang1* ,  Di Wang2 ,  Jicai Ding2
Show Less
1 China United Coalbed Methane Corporation Ltd., Beijing, China
2 China National Offshore Oil Corporation Research Institute Corporation Ltd., Beijing, China
3 China National Offshore Oil Corporation Gas And Power Group Corporation Ltd., Beijing, China
JSE 2026, 35(2), 025460108 https://doi.org/10.36922/JSE025460108
Submitted: 11 November 2025 | Revised: 3 February 2026 | Accepted: 7 February 2026 | Published: 31 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
Cite
XML
Abstract

The Linxing–Shenfu gas field, a key block for China National Offshore Oil Corporation’s onshore unconventional oil and gas exploration, is characterized by complex geology that poses dual challenges to the efficiency and accuracy of traditional seismic interpretation methods. This study presents a systematic review of the application progress of machine learning, particularly deep learning, in seismic interpretation within this block since 2018. To address the specific geological characteristics and exploration needs, we developed a comprehensive intelligent interpretation workflow. This workflow integrates intelligent horizon and fault interpretation, deep clustering for seismic facies analysis, and automated identification of special geological bodies (e.g., Zijinshan igneous rock mass), enabling the accurate reconstruction of the stratigraphic framework. Furthermore, leveraging deep learning models, we achieved direct prediction of lithology, physical properties (e.g., porosity, permeability), and gas-bearing parameters, culminating in the comprehensive characterization of geological “sweet spots.” Practical applications demonstrate that this intelligent interpretation workflow not only significantly enhances interpretation efficiency but also provides distinct advantages for overcoming the bottlenecks of traditional theoretical methods, such as handling low signal-to-noise ratio data, identifying thin interbeds, and predicting “sweet spots.” This review provides robust support for efficient exploration and development decision-making in the Linxing–Shenfu Block.

Keywords
Unconventional reservoir
Seismic interpretation
Machine learning
Deep learning
Sweet spot
Funding
None.
Conflict of interest
The authors declare no potential conflict of interest.
References
[1]
  1. Liu JZ, Zhu GH, Liu YC, et al. Breakthrough, future challenges and countermeasures of deep coalbed methane in the eastern margin of Ordos Basin: A case study of Linxing-Shenfu block. Acta Pet Sin. 2023;44(11):1827-1839. [In Chinese]. doi: 10.7623/syxb202311006

 

  1. Mi L, Zhu G. Geological characteristics and exploration breakthrough in Linxing-Shenfu tight gas field, northeastern Ordos Basin. China Pet Explor. 2021;26(3):53-67. [In Chinese]. doi: 10.3969/j.issn.1672-7703.2021.03.005

 

  1. Wang SX, Yuan SY. Ren gong zhi neng di qiu wu li kan tan. Artificial Intelligence for Geophysical Exploration. Beijing: Science Press; 2025. [In Chinese].

 

  1. Yu SW, Ma JW. Deep learning for geophysics: Current and future trends. Rev Geophys. 2021;59(3):e2021RG000742. doi: 10.1029/2021RG000742

 

  1. Zhu YH, Zhao ZG, Lu YY, et al. Research progress and key technologies for exploration of onshore tight sandstone gas by CNOOC. China Offshore Oil Gas. 2024;36(6):13-25. [In Chinese]. doi: 10.11935/j.issn.1673-1506.2024.00.007

 

  1. Zhu GH, Li BL, Li ZC, et al. Practices and development trend of unconventional natural gas exploration in eastern margin of Ordos Basin: Taking Linxing-Shenfu gas field as an example. China Offshore Oil Gas. 2022;34(4):16-29. [In Chinese]. doi: 10.11935/j.issn.1673-1506.2022.04.002

 

  1. Liu C, Zhang DM, Li C, Lu YY, Guo MQ. Upper Paleozoic tight gas sandstone reservoirs and main controls, Linxing block, Ordos Basin. Oil Gas Geol. 2021;42(5):1146-1158. [In Chinese]. doi: 10.11743/ogg20210512

 

  1. Ge Y, Zhu GH, Wan H, Pan XZ, Huang ZL. The influence of Zijinshan Structural belt to the formation and distribution of tight sandstone gas reservoir in Upper Paleozoic, in the eastern Ordos Basin. Nat Gas Geosci. 2018;29(4):491-499. [In Chinese]. doi: 10.11764/j.issn.1672-1926.2018.03.008

 

  1. Bergen KJ, Johnson PA, de Hoop MV, Beroza GC. Machine learning for data-driven discovery in solid Earth geoscience. Science. 2019;363(6433):eaau0323. doi: 10.1126/science.aau0323

 

  1. Herron DA. Pitfalls in horizon autopicking. Interpretation. 2015;3(1):SB1-SB4. doi: 10.1190/INT-2014-0062.1

 

  1. Hoyes J, Cheret T. A review of ‘global’ interpretation methods for automated 3D horizon picking. Lead Edge. 2011;30(1):38-47. doi: 10.1190/1.3535431

 

  1. Wu XM, Fomel S. Least-squares horizons with local slopes and multi-grid correlations. Geophysics. 2018;83(4):IM29-IM40. doi: 10.1190/geo2017-0830.1

 

  1. Xu YM. Di zhen pou mian duan ceng de jie shi yan jiu. Interpretation of faults in seismic profiles. China Pet Chem Stand Qual. 2012;33(10):50. [In Chinese]. doi: 10.3969/j.issn.1673-4076.2012.10.040

 

  1. Xi GM, He SG, Min Y, Sun YC, Li QC. Fault identification by the coherency attributes. Northwest Geol. 2019;52(1):244-249. [In Chinese]. doi: 10.19751/j.cnki.61-1149/p.2019.01.023

 

  1. Cai HP. Improvement of variance cube technique and its application in seismic interpretation. Coal Geol Explor. 2008;36(1):74-76. [In Chinese]. doi: 10.3969/j.issn.1001-1986.2008.01.019

 

  1. He Y, He ZH, Xiong XJ. Fault Identification Based on High-precision Curvature Analysis. J Oil Gas Technol. 2010;32(6):404-407. [In Chinese]. doi: CNKI:SUN:JHSX.0.2010-06-102

 

  1. Li HW, Bai XL, Cui JB, Wan HZ, Yuan SH, Chu WC. Fault identification technology of ant attribute optimization. Coal Geol Explor. 2019;47(6):174-179. [In Chinese]. doi: 10.3969/j.issn.1001-1986.2019.06.026

 

  1. Bi ZF, Wu XM, Geng ZC, Li HS. Deep relative geologic time: A deep learning method for simultaneously interpreting 3-D seismic horizons and faults. J Geophys Res Solid Earth. 2021;126(9):e2021JB021882. doi: 10.1029/2021JB021882

 

  1. Geng ZC, Wu XM, Shi YZ, Fomel S. Deep learning for relative geologic time and seismic horizons. Geophysics. 2020;85(4):WA87-WA100. doi: 10.1190/geo2019-0252.1

 

  1. Luo YL, Zhang GL, Li L, Zhang XD, Duan J, Li XW. Attention-based two-stage U-Net horizon tracking. IEEE Geosci Remote Sens Lett. 2022;19:1-5. doi: 10.1109/LGRS.2022.3217376

 

  1. Wu XM, Liang LM, Shi YZ, Fomel S. FaultSeg 3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics. 2019;84(3):IM35-IM45. doi: 10.1190/geo2018-0646.1

 

  1. Li Y, Wu XM, Zhu ZY, Ding JC, Wang QZ. FaultSeg 3D plus: a comprehensive study on evaluating and improving CNN-based seismic fault segmentation. Geophysics. 2024;89(5):N77-N91. doi: 10.1190/geo2022-0778.1

 

  1. Wei XL, Zhang CX, Kim SW, et al. Seismic fault detection using convolutional neural networks with focal loss. Comput Geosci. 2022;158:104968. doi: 10.1016/j.cageo.2021.104968

 

  1. Li SZ, Liu NH, Li FY, Gao JH, Ding JC. Automatic fault delineation in 3-D seismic images with deep learning: Data augmentation or ensemble learning? IEEE Trans Geosci Remote Sens. 2022;60:1-14. doi: 10.1109/TGRS.2022.3150353

 

  1. Zhou ZW, Rahman Siddiquee MM, Tajbakhsh N, Liang JM. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. arXiv. Preprint posted online July 18, 2018. Accessed March 19, 2021. doi: 10.48550/arXiv.1807.10165

 

  1. Liu BG, Liu LH. Application of Applied Seismic Sedimentology in Sedimentary Facies Analysis. Geophys Prospect Pet. 2008;47(3):266-271. [In Chinese]. doi: 10.3969/j.issn.1000-1441.2008.03.010

 

  1. Wrona T, Pan I, Gawthorpe RL, Fossen H. Seismic facies analysis using machine learning. Geophysics. 2018;83(5):O83-O95. doi: 10.1190/geo2017-0595.1

 

  1. Zhang LK, Qin LJ, Zhang GS, Sun XQ. On Seismic Facies Analysis Based on Seismic Attributes. Chin J Eng Geophys. 2010;7(6):694-698. [In Chinese]. doi: 10.3969/j.issn.1672-7940.2010.06.009

 

  1. Barnes A. Redundant and useless seismic attributes. Geophysics. 2007;72(3):P33-P38. doi: 10.1190/1.2716717

 

  1. Yan XY, Gu HM, Luo HM, Yan YP. Intelligent Seismic facies classification based on an improved deep learning method. Oil Geophys Prospect. 2020;55(6):1169-1177. [In Chinese]. doi: 10.13810/j.cnki.issn.1000-7210.2020.06.001

 

  1. Shuo LX, Zhao YH, Chai BF, Li ZH, Wu H. Semi-supervised adversarial network for seismic facies classification. Prog Geophys. 2023;38(5):2105-2113. [In Chinese]. doi: 10.6038/pg2023GG0372

 

  1. Qian F, Yin M, Liu XY, Wang YJ, Lu C, Hu GM. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics. 2018;83(3):A39-A43. doi: 10.1190/geo2017-0524.1

 

  1. Liu ML, Jervis M, Li WC, Nivlet P. Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks. Geophysics. 2020;85(4):O47-O58. doi: 10.1190/geo2019-0627.1

 

  1. Li QX, Luo YN, Ma XQ, Chen C, Zhu YH. Seismic facies clustering technology based on deep embedding network. Oil Geophys Prospect. 2022;57(2):251-260. [In Chinese]. doi: 10.13810/j.cnki.issn.1000-7210.2022.02.002

 

  1. Wu XM, Yang JR, Zhu ZY, Ding JC, Wang QZ. Discussions on computational seismic structural interpretation and modeling. Geophys Prospect Pet. 2022;61(3):392-407. [In Chinese]. doi: 10.3969/j.issn.1000-1441.2022.03.002

 

  1. Jones IF, Davison I. Seismic imaging in and around salt bodies. Interpretation. 2014;2(4):SL1-SL20. doi: 10.1190/INT-2014-0033.1

 

  1. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer; 2015:234-241. doi: 10.1007/978-3-319-24574-4_28

 

  1. Shi YZ, Wu XM, Fomel S. Automatic salt-body classification using a deep convolutional neural network. In: SEG Technical Program Expanded Abstracts 2018. SEG International Exposition and Annual Meeting 2018; August 1, 2018. Society of Exploration Geophysicists; 2018:1971-1975. doi: 10.1190/segam2018-2997304.1

 

  1. Di HB, Wang Z, AlRegib G. Deep convolutional neural networks for seismic salt-body delineation. In: Proceedings of the AAPG 2018 Annual Convention & Exhibition. AAPG 2018 Annual Convention & Exhibition; May 20–23, 2018; Salt Lake City, Utah, United States. AAPG; 2018. doi: 10.1306/70630Di2018

 

  1. Shi YZ, Wu XM, Fomel S. SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network. Interpretation. 2019;7(3):SE113-SE122. doi: 10.1190/INT-2018-0235.1

 

  1. Yang XK, Yang YH, Ji LD, Su CQ, Zheng ML, Zhao L. Stages and characteristics of thermal actions in eastern part of Ordos Basin. Acta Geol Sin. 2006;80(5):705-711. [In Chinese]. doi: 10.3321/j.issn:0001-5717.2006.05.010

 

  1. Yang XK, Chao HX, Zhang ZF, Yao WH, Dong M. Characteristics of the Zijinshan complex and its dynamic environment in the east of the Ordos Basin—Analysis of a classic example on the deep processes of thermal-magma activity in the basin. Geotecton Metallog. 2010;34(2):269-281. [In Chinese]. doi: 10.16539/j.ddgzyckx.2010.02.008

 

  1. Chen G, Ding C, Xu LM, et al. Analysis on the thermal history and uplift process of Zijinshan intrusive complex in the eastern Ordos basin. Chin J Geophys. 2012;55(1):78-90. [In Chinese]. doi: 10.6038/j.issn.0001-5733.2012.11.020

 

  1. Ren ZL, Zhang S, Gao SL, Cui JP, Xiao YY, Xiao H. E’erduosi pen di gou zao re yan hua shi ji qi cheng kuang cheng cang yi yi. Tectono-thermal evolution history of the Ordos Basin and its implications for hydrocarbon accumulation and mineralization. Sci China Press. 2007;37(suppl 1):23-32. [In Chinese].

 

  1. Cooke DA, Schneider WA. Generalized linear inversion of reflection seismic data. Geophysics. 1983;48(6):665-676. doi: 10.1190/1.1441497

 

  1. Mukerji T, Avseth P, Mavko G, Takahashi I, Gonzalez EF. Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization. Lead Edge. 2001;20(3):313-319. doi: 10.1190/1.1438938

 

  1. Avseth P, Mukerji T, Mavko G. Quantitative Seismic Interpretation. Cambridge University Press; 2005.

 

  1. Grana D. Joint facies and reservoir properties inversion. Geophysics. 2018;83(3):M15-M24. doi: 10.1190/GEO2017-0670.1

 

  1. Grana D. Bayesian linearized rock-physics inversion. Geophysics. 2016;81(6):D625-D641. doi: 10.1190/GEO2016-0161.1

 

  1. Aleardi M, Ciabarri F. Application of different classification methods for litho-fluid facies prediction: a case study from the offshore Nile Delta. J Geophys Eng. 2017;14(5):1087-1102. doi: 10.1088/1742-2140/aa7301

 

  1. Larsen AL, Ulvmoean M, Omre H, Buland A. Bayesian lithology/fluid prediction and simulation on the basis of a Markov-Chain prior model. Geophysics. 2006;71(5):R69-R78. doi: 10.1190/1.2245469

 

  1. Wang L, Zhang F, Li XY, Di BR, Zeng LB. Quantitative seismic interpretation of rock brittleness based on statistical rock physics. Geophysics. 2019;84(4):IM63-IM75. doi: 10.1190/geo2018-0094.1

 

  1. Kong W, Zhang LJ, Cheng MW, Zhang HW, Jiang LZ. Application of Waveform Indication Inversion in the Prediction of Thin Carbonate Reservoirs in the Amu Darya Right Bank Area. In: Lin J, ed. Proceedings of the International Field Exploration and Development Conference 2019. IFEDC 2019; October 16–18, 2019; Xi’an, China. Springer; 2020:3256-3266. doi: 10.1007/978-981-15-2485-1_302

 

  1. Qi Y, Wu K, Wang B, Zheng XW, Li WL, Li D. Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion. Front Earth Sci. 2024;12:1440729. doi: 10.3389/feart.2024.1440729

 

  1. Sena A, Castillo G, Chesser K, et al. Seismic reservoir characterization in resource shale plays: ‘sweet spot’ discrimination and optimization of horizontal well placement. In: SEG Technical Program Expanded Abstracts 2011. 81th SEG Annual International Meeting; September 18–23, 2011; San Antonio, Texas, United States. Society of Exploration Geophysicists; 2011:1744-1748. doi: 10.1190/1.3627542

 

  1. Hart BS, Pearson R, Rawling GC. 3-D seismic horizon-based approaches to fracture-swarm sweet spot definition in tight-gas reservoirs. Lead Edge. 2002;21(1):28-35. doi: 10.1190/1.1445844

 

  1. Le J, Jing YQ, Liang X, Fan HJ, Cai WT. Using seismic response characteristics to predict “sweet spot” locations in a low-permeability reservoir in the Bohai Sea. Geophys Prospect Pet. 2022;61(5):876-887. [In Chinese]. doi: 10.3936/j.issn.1000-1441.2022.05.012

 

  1. Liu ZQ, Wang YM, Bai GC, Gong CL. Application of sweetness and its fusion attribute in the research of Deepwater reservoir. Oil Geophys Prospect. 2010;45(suppl 1):158-162. doi: 10.13810/j.cnki.issn.1000-7210.2010.s1.005

 

  1. Peng CZ, Peng J, Chen YH, Zhang HR. Seismic predicting of sweet spots in the Da’anzhai Shale Play, Yuanba area, Sichuan Basin. Nat Gas Ind. 2014;34(6):42-47. [In Chinese]. doi: 10.3787/j.issn.1000-0976.2014.06.007

 

  1. Zhao C, Jin FM, Han GM, Guo SW, Xing X, Liu HZ. Direct prediction of sweet spots in sandstone reservoirs based on pre-stack probability inversion. Oil Geophys Prospect. 2023;58(5):1211-1219. [In Chinese]. doi: 10.13810/j.cnki.issn.1000-7210.2023.05.018

 

  1. Qian YG. Application of machine deep learning technology in tight sandstones reservoir prediction: A case study of Xujiahe Formation in Xinchang, western Sichuan Depression. Pet Reserv Eval Dev. 2023;13(5):600-607. [In Chinese]. doi: 10.13809/j.cnki.cn32-1825/te.2023.05.007

 

  1. Wang D, Zhang YM, Zhang FC, Ding JC, Niu C. Quantitative prediction of tight sandstone sweet spots based on deep learning method with prior information constraints. Oil Geophys Prospect. 2023;58(1):65-74. [In Chinese]. doi: 10.13810/j.cnki.issn.1000-7210.2023.01.006

 

  1. An P, Cao DP. Ji yu tan xing zu kang he shen du xue xi de mi du yu ce fang fa yan jiu. Research on density prediction methods based on elastic impedance and deep learning. In: Proceedings of Annual Meeting of Chinese Geoscience Union 2018, October 21–23, 2018; Beijing, China. China Peace Publishing House; 2018:754-757. [In Chinese].

 

  1. Zhang MG, Hong Z, Dou YT, Cui XJ. Application of time-frequency analysis technology to the gas detection in Sulige area. Lithol Reserv. 2013;25(5):76-85. [In Chinese].

 

  1. Zhang YQ, Wang ZZ, Zhang N. Research on sand body identification and gas bearing area prediction of He 8 member in SU X Well Block of Sulige Gas Field. Nat Gas Geosci. 2011;22(1):164-170. [In Chinese].

 

  1. Lin LM, Zheng Y, Shi H, et al. Gas-bearing prediction of thin tight sandstone reservoirs based on facies-controlled geostatistical pre-stack inversion: A case study of the middle Linxing Block in the Ordos Basin. Nat Gas Ind. 2023;43(2):56-66. [In Chinese]. doi: 10.3787/j.issn.1000-0976.2023.02.006

 

  1. Deng BQ, Ding D. Reservoir prediction method based on seismic-gas logging fusion. Mud Logging Eng. 2025;36(1):77-84. [In Chinese]. doi: 10.3969/j.issn.1672-9803.2025.01.012

 

  1. Song ZH, Yuan SY, Li ZM, Wang SX. KNN-based gas-bearing prediction using local waveform similarity gasindication attribute - An application to a tight sandstone reservoir. Interpretation. 2022;10(1):T1-T10. doi: 10.1190/INT-2021-0045.1

 

  1. Gao JH, Song ZH, Gui JY, Yuan SY. Gas-bearing prediction using transfer learning and CNNs: An application to a deep tight dolomite reservoir. IEEE Geosci Remote Sens Lett. 2022;19:3001005. doi: 10.1109/LGRS.2020.3035568
Next article in this issue
Share
Back to top
Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept