Intelligent Identification Technology of Stratum Sub-Layer Based on Multi-Parameter Integration of Logging While Drilling


  • Haibo Liang
  • Xin Jiang
  • Yi Yang
  • Jialing Zou



Logging While Drilling; Sub-Layer Division; LightGBM; Parameter Optimization; Intelligent Identification.


 Stratum identification is the division of the stratum lithology of one region, which is an important part of petroleum geology research. How to effectively improve the accuracy and efficiency of stratum recognition is an important issue in oil exploration and development. During the traditional oil and gas drilling process, the logging data is commonly used as the main basis to conduct artificial stratum division. The challenges encountered are high labor intensity and excessive dependence on artificial experience for identification accuracy. By comprehensively considering the synergy of multiple parameters in oil and gas drilling, we propose an intelligent sub-layer division model based on the LightGBM algorithm. First, the data set was formed by normalizing, de-noising, and smoothing the drilling engineering parameters and combining them with the element logging parameters. Then, the LightGBM algorithm was applied to build the sub-layer division model, and the deep neural network and support vector machine was introduced for comparative analysis. Finally, the input parameters of the model were optimized by the principal component analysis method to realize the intelligent identification of the stratum sub-layer. The application results of a certain block in the central Bohai Sea oil field showed that the intelligent identification of stratum sub-layer while drilling could be realized. The use of the model and combination of the logging while drilling data with high recognition accuracy provided a crucial theoretical model for the transformation of stratum sub-layer identification technology.


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How to Cite

Intelligent Identification Technology of Stratum Sub-Layer Based on Multi-Parameter Integration of Logging While Drilling. (2023). Academic Journal of Science and Technology, 8(3), 118-127.

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