| Microbial structures are developed in microbial carbonate rocks, with strong diagenesis superimposed, and
their lithology-electrical property relationship is more complex. Conventional logging has been unable to distinguish
microbial structure characteristics. Although electric imaging logging has high resolution and can identify microbial
structures, there is also a problem of multiple solutions. At present, the combination of conventional logging and electrical
imaging logging is the most effective and accurate identification method. The main methods of combination include chart
method and artificial intelligence learning method. However, the efficiency of chart method is low, and artificial
intelligence methods also have two problems: (1) there is difficulty in integrating logging data from different dimensions;
(2) the core sampling data is limited, and the number of training samples is insufficient. Therefore, this article selects the
K-Neighbor Classification Algorithm (KNN), a machine learning method that adapts to few samples, and proposes a
method of separate training and recognition, and re-fusion of recognition results. Firstly, based on core data, we establish
lithofacies classification schemes and rock structure feature classification schemes respectively, and establish a core
training sample parameter library, and then use KNN method to identify lithofacies types with conventional logging and
rock structure types with electrical imaging logging. Finally, based on expert experience, we fuse the two recognition
results to obtain finely classified lithofacies types. Taking the Dengying Member 4 in the GM area of Sichuan Basin as an
example, 6 types of lithofacies and 7 types of rock structural feature types were identified. Based on expert experience
fusion, 9 types of finely classified lithofacies were finally identified, with a recognition accuracy rate over 85%. This study
has effectively supported the fine research work on sedimentary microfacies of the Dengying Member 4 in the GM area
and promoted the exploration and development work in Sichuan Basin. This method leverages the advantages of
conventional logging and electrical imaging logging, achieving efficient and high-precision identification of lithofacies,
and is worth promoting. |
