TITLE:
Efficient Identification Method of Interbeds Based on Neural Network Combined with Grey Relational Analysis—Taking the Lower Sub-Member of the Sangonghe Formation in Moxizhuang Oilfield as an Example
AUTHORS:
Yuanbo Song, Yankai Zhu, Binxin Zeng
KEYWORDS:
Interlayer Recognition, Grey Relational Analysis, Fully Connected Neural Network, Second Member of Sangonghe Formation
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.2,
February
17,
2025
ABSTRACT: The storage layer within the Moxizhuang Oilfield in the Junggar Basin develops various types of interlayer barriers with significant differences in morphology and scale of development. In response to the issues of interlayer barriers affecting the formation of oil and gas reservoirs and controlling oil-water distribution, this study proposes precise classification and quantitative identification of interlayer barriers in the study area based on a fully connected neural network combined with grey relational analysis. Taking the second member of the Sangonghe Formation (J1S22) in the Moxizhuang Oilfield as an example, combined with previous research, this study statistically analyzes the lithology and logging response characteristics of three types of interlayer barriers in the study area. Based on differences in composition, lithology, and genesis, interlayer barrier types are classified. Sensitive logging data such as natural gamma, acoustic time difference, and resistivity are selected through crossover plots. Grey relational analysis is used to calculate comprehensive discrimination indicators for interlayer barriers. Combined with the fully connected neural network method, an interlayer barrier identification model is established, and model training is conducted to verify the accuracy of interlayer barrier identification. The results indicate that the interlayer barrier identification model based on a fully connected neural network can rapidly and accurately identify interlayer barriers and their types. Its application in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin has proven that the identification results of this method for interlayer barriers have a conformity rate exceeding 90% with core data, demonstrating excellent performance in interlayer barrier identification and proving the effectiveness of the model for interlayer barrier identification and prediction in this area. The research conclusions can provide theoretical guidance and technical reference for the identification and evaluation of interlayer barriers in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin.