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Development of geosteering system based on GWO–SVM model

  • S.I.: Machine Learning based semantic representation and analytics for multimedia application
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Abstract

Two geological steering models, the particle swarm support vector machine (PSO-SVM) and the gray wolf support vector machine (GWO–SVM), were analyzed to determine the models’ optimal global parameters. The fitness function was used to compare and analyze the models, while the original data and feature reconstruction data were used to simulate and analyze the models. The most suitable model for shale gas geosteering identification was selected; the multi-source information fusion for the shale gas geosteering design was completed; and a complete geosteering discrimination model was established. Research on the shale gas geosteering identification system with multi-source information fusion was conducted, and we completed the technical scheme design of shale gas geosteering discrimination system with multi-source information fusion, supported the ground real-time data acquisition system, processed the geosteering discrimination, and completed the demand analysis of the shale gas geosteering discrimination system with multi-source information fusion. In the process of drilling, the relationship between the trajectory and the formation position was analyzed, and the drilling trajectory was monitored. The system mainly included the software main interface, data, and geosteering identification module.

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Authors and Affiliations

  1. China-French Bohai Geoservices Co., LTD, Tianjin, 300450, China

    Min Mao, Fengyang Xu, Pengbo Ni & Haosheng Wu

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

    Hai Yang

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  1. Min Mao
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  2. Hai Yang
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  3. Fengyang Xu
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  4. Pengbo Ni
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  5. Haosheng Wu
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Correspondence to Hai Yang.

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Mao, M., Yang, H., Xu, F. et al. Development of geosteering system based on GWO–SVM model. Neural Comput & Applic 34, 12479–12490 (2022). https://doi.org/10.1007/s00521-021-06583-6

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  • DOI: https://doi.org/10.1007/s00521-021-06583-6

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