# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 3 Methods for Determining Causes of Abnormal Formation Pressure
### Mining Similar Abnormal Pressure Mechanism Samples Based on Cluster Analysis  
By collecting well logging data for the entire depth of the target well, the initial focus is on the variation trend of P wave velocity with depth. By analyzing the changes in P wave velocity with depth, the normal compaction section and abnormal high-pressure section of the formation can be identified. This method helps identify specific areas in the formation where abnormal pressure may exist, providing important clues for subsequent analysis.  
To further study the mechanisms causing abnormal high pressure, the key geophysical parameters of velocity and density are utilized. By applying clustering algorithms for unsupervised learning, the abnormal high-pressure mechanisms are divided into different groups, as illustrated in Figure 3. The advantage of this method is that it can automatically cluster similar data samples together without prior knowledge of specific categories.  
Hierarchical clustering is an unsupervised learning algorithm used to hierarchically divide data samples into different categories, forming a clustering tree structure. This method incrementally merges or splits data to construct different levels of clusters. Hierarchical clustering has two main methods: agglomerative clustering and divisive clustering. Agglomerate clustering starts with each data point and gradually merges the most similar data points into clusters, continuing this process until all data points are merged into one large cluster. This method is suitable for small datasets but may be less efficient for large datasets. Divisive clustering starts with all data points in one large cluster and gradually splits it into smaller clusters, continuing this process until each data point forms its own cluster. This method is suitable for large datasets but may lead to over-segmentation in some cases. Key steps in hierarchical clustering include defining similarity or distance metrics between data points, such as Euclidean distance, Manhattan distance, and correlation coefficients. Starting from each data point, clusters are incrementally merged or split based on similarity or distance, constructing a clustering tree. The clustering tree can be visualized through a dendrogram, helping to understand the clustering structure of the data. Hierarchical clustering helps identify groups of similar samples, aiding in  
Figure 3: Application of clustering algorithm.  
recognizing different mechanisms causing abnormal high pressure. These clusters can reveal the combined effects of geological features, formation properties, and fluid migration in different abnormal high-pressure sections. The results of this method contribute to a more comprehensive understanding of the mechanisms causing abnormal high pressure, providing valuable references for geological exploration and engineering decisions. It is important to note that while hierarchical clustering can help discover groups of similar samples, geological background knowledge and the expertise of geologists are still needed to interpret the model results and validate hypotheses. By considering multiple factors, more accurate inferences on the causes of abnormal high pressure can be made.