Study on the Reservoir Interlayer in The Southern Part of Tianci Bay in the Ordos Basin

Authors

  • Haojie Si
  • Zhen Yuan

DOI:

https://doi.org/10.54097/7wxs8z74

Keywords:

Heterogeneity; logging curve; automatic identification; gradient discrimination.

Abstract

The study of reservoir interlayers is an indispensable content to reveal reservoir heterogeneity. The main formation of Chang 4+5 in Tianciwan South Oilfield of Jingbian Oil Production Plant has entered the development stage of high water cut, and the distribution of remaining oil is complex. In order to meet the needs of oilfield development and production, it is necessary to study the development status of interlayer in this formation. At present, the study of interlayers mainly relies on manual analysis of logging curves. Due to the variety of logging curves and the huge amount of data, the research process is time-consuming and low efficiency. This paper takes the Chang4+5 reservoir group of Tianci bay South Oil field of Jingbian Oil Production plant as the main research object, aiming at the existing problems of heavy workload and low accuracy, an automatic separation identification method is proposed. This method is based on logging data, and through digital filtering and gradient discrimination, it realizes rapid and accurate identification of interlayers.

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References

[1] Sun Niyuan. Research on reservoir interlayers in the delta front of Dongying Formation, Suizhong 36-1 Oilfield [D]. China university of petroleum (Beijing), 2021. DOI: 10.27643 /, dc nki. Gsybu. 2021.000318,pp.43-48

[2] YU Qiujun. Standardized processing method of multi-well logging data: A case study of M Oilfield [J]. Petroleum Geology and Engineering, 2019,34(06):118-122.

[3] Xue L W. Design and implementation of logging data interpretation system based on React framework [D]. Xian university of electronic science and technology, 2022. DOI: 10.27389 /, dc nki. Gxadu. 2022.002326, pp.38–40.

[4] DU Jiann.Research on logging evaluation technology of flooded layer based on curve reconstruction method [D]. China university of petroleum (Beijing), 2019. DOI: 10.27643 /, dc nki. Gsybu. 2019.001291,pp.31-51.

[5] Chen Yan, Jiao Shixiang, Cheng Chao et al. Semi-supervised interlayer identification method based on autoencoder [J]. Special Oil and Gas Reservoirs,2021,28(01):86-91.

[6] Yasser,Aya,Leila, et al. Reservoir heterogeneity analysis and flow unit characteristics of the Upper Cretaceous Bahariya Formation in Salam Field, north Western Desert, Egypt[J]. Arabian Journal of Geosciences, 21,14(16).

[7] Li Ning, Xu Binsen, Wu Hongliang et al. Application status and prospect of artificial intelligence in well logging formation evaluation [J]. Acta Petrolei Sinica, 2021,42(04):508-522.

[8] Mitten A,Mullins J,Pringle J, et al. Depositional conditioning of three dimensional training images: Improving the reproduction and representation of architectural elements in sand-dominated fluvial reservoir models[J]. Marine and Petroleum Geology, 2020,113.

[9] Hosseinyar G,Moussavi‐Harami R,Fard A I, et al. Facies analyses and depositional setting of the L ower C retaceous S hurijeh– S hatlyk formations in the K opeh Dag–A mu D arya B asin ( I ran and T urkmenistan)[J]. Geological Journal, 2019, 54(3).

[10] Wu Xueting. Study on distribution characteristics and genesis of Jurassic calcareous interlayer in Shinan area [D]. Xi 'an petroleum university, 2020. DOI: 10.27400 /, dc nki. Gxasc. 2020.000565.

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Published

06-11-2024

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Section

Articles

How to Cite

Si, H., & Yuan, Z. (2024). Study on the Reservoir Interlayer in The Southern Part of Tianci Bay in the Ordos Basin. Academic Journal of Science and Technology, 13(1), 175-180. https://doi.org/10.54097/7wxs8z74