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Study on the intelligent identification method of formation lithology by element and gamma spectrum

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Abstract

The rapid and accurate identification of the formation lithology encountered during the drilling of oil and gas fields is an important step to control the trajectory of the drilling tool borehole and improve the optimal reservoir encounter rate. At present, the main way to distinguish the lithology of the formation encountered by drilling is to use artificial detection elements, which does not form a set of intelligent formation identification system. In view of the above problems, this paper proposes a method to identify the lithology of drilled formation by using element and gamma spectrum measurement and establishes a reasoning model of intelligent identification of formation lithology by using improved fuzzy clustering algorithm-SVM (IFCM-SVM) method. Field application shows that the accuracy of IFCM-SVM intelligent formation identification method proposed in this paper can reach 90.9%, and verifies the feasibility of using element and gamma spectrum measurement to realize the intelligent identification of formation lithology in drilling.

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Acknowledgements

This work was supported by Sichuan Science and Technology Innovation and Venture Seedling Project, China (No. 2020JDRC0081). At the same time, the work was supported by the graduate innovation fund of southwest petroleum university, China (No. 2019cxyb003).

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Authors and Affiliations

  1. School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China

    He Zhang, Qiuhong Chen, Haibo Liang & Jialing Zou

  2. China-French Bohai Geoservices Co., Tianjin, China

    Pengbo Ni & Min Mao

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  1. He Zhang
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  2. Qiuhong Chen
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Correspondence to He Zhang.

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Zhang, H., Chen, Q., Ni, P. et al. Study on the intelligent identification method of formation lithology by element and gamma spectrum. Neural Comput & Applic 34, 3375–3383 (2022). https://doi.org/10.1007/s00521-021-05714-3

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