# 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
#### 3.1.2 Establishing a Cause Mechanism Identification Model Based on BO-LightGBM  
For the given problem of categorizing the causes of abnormal pressure, a decision tree algorithm suitable for treating numerical values as categorical symbols was chosen, combined with gradient boosting and histogram techniques, to construct an efficient and accurate LightGBM model. During model construction, the advantages of decision tree algorithms were fully utilized to map continuous numerical features to categories of abnormal mechanisms. By binning the numerical values, the model can treat these numbers as categorical symbols, better fitting the needs of the problem. LightGBM is an improved version of the gradient boosting tree algorithm that optimizes the algorithm and data structure to enhance training speed and memory efficiency. Below are the principles of the GOSS algorithm: LightGBM adopts the GOSS vertical parallelization training algorithm, which selects a portion of samples with large gradient values for training and samples those with small gradient values to reduce dataset size, maintaining model accuracy while significantly reducing training time and memory consumption. In the histogram optimization algorithm, LightGBM uses the GOH histogram optimization algorithm. Instead of sorting feature values traditionally, it bins feature values into histograms, reducing sorting complexity and increasing training speed, as shown in Figure 4.  
1. Learning Rate and Early Stopping: LightGBM allows users to set learning rates and early stopping strategies to control the training process, limiting each weak learner's contribution and avoiding overfitting. 2. Feature Parallelization and Histogram Compression: LightGBM employs feature parallelization and histogram compression techniques, enhancing training speed and memory efficiency. 3. Leaf-Wise Growth Strategy: LightGBM uses a leaf-wise growth strategy, splitting the leaf with the largest gradient each time, focusing on samples with larger gradients and accelerating learning. 4. Depth-Limited Decision Trees: LightGBM uses depth-limited decision trees to reduce memory consumption and model complexity. 5. Parallel Learning and Cache Optimization: LightGBM increases training efficiency through parallel learning and cache optimization, updating multiple leaf nodes' statistics simultaneously and reducing data reads. Overall, LightGBM significantly improves training speed and memory efficiency through GOSS and GOH algorithms, the leaf-wise growth strategy, feature parallelization, and histogram compression, excelling in handling large-scale datasets and high-dimensional features. However, LightGBM's parameter combinations are complex and rely on manual experience, with exhaustive optimization taking considerable time. Intelligent optimization algorithms play a crucial role in machine learning by optimizing model parameters, architectures, and hyperparameters, improving performance, reducing training time, and enhancing generalization. These algorithms balance exploitation and exploration, finding the best combinations in complex parameter spaces for better prediction and classification. Probability surrogate models and acquisition functions are core components of Bayesian optimization. Among them, surrogate models can be parametric or non-parametric, with Gaussian processes being the most widely used non-parametric model. Acquisition functions map from input, observation, and hyperparameter space to real number space, balancing exploitation and exploration to evaluate the distribution of points. Bayesian optimization using Gaussian processes as surrogates has several advantages. First is flexibility: a Gaussian process is a non-parametric model that does not require a predefined specific functional form. This allows the Gaussian process to adapt to various complex functional relationships, including non-linear, non-convex, and noisy conditions. Because Gaussian processes can fit any continuous function, they offer great flexibility in optimization problems.