水平井钻探已成为油公司提高致密与非常规油气单井产量的重要手段，但由于水平井井眼轨迹与地层的空间关系复杂，传统直井分析思路无法有效应用，准确描述水平井井眼轨迹与目的层及围岩的空间组合关系是水平井测井解释的首要任务。基于导眼井构建地层初始模型，利用测井资料正演约束逐段调整模型是目前主流做法，但该方法时效性低，针对同一地区不同井都需要大量重复的正演计算。因此，在水平井测井数据的处理和解释中，建立合理的井地模型是关键。井地模型能准确描述井眼与地层界面之间的空间关系，包括井眼位置与地层界面距离，井轴位置与地层法线方向的夹角等。同时，基于机器学习和人工智能技术的测井数据分析方法，通过智能模型的训练，已经应用于测井数据解释的各个方面，借助人工智能技术有望突破传统方法的瓶颈。因此，提出一种基于多模型集成与深度神经网络的自动化水平井测井解释方法：首先构建包含不同井眼轨迹与地层组合关系的理论模型，生成测井响应样本库；然后整合极端梯度提升树（eXtreme Gradient Boosting，XGBoost）、轻量级梯度提升机（Light Gradient Boosting Machine，LightGBM）、分类提升机（Categorical Boosting，CatBoost）等机器学习模型，通过多层感知器（Multi-Layer Perceptron，MLP）进一步融合各模型的预测结果；最后对实际测井资料开展井轨迹与围岩几何关系的智能自动判识。实例分析显示，该方法在准确捕捉水平井复杂测井响应特征的同时，显著提高了解释速度和精度，能够适应相似地质环境下多口井的快速分析需求，为水平井测井解释提供了一种高效的智能化手段。

Horizontal well drilling has become an important method for oil companies to enhance single-well production in tight and unconventional oil and gas reservoirs. However, due to the complex spatial relationship between the wellbore trajectory of horizontal wells and the formation layers, traditional vertical well analysis methods cannot be effectively applied. Accurately describing the spatial combination relationship between the wellbore trajectory, the target layer, and the surrounding rock is a primary task in horizontal well logging interpretation. To address this issue, the mainstream approach is to construct an initial stratigraphic model based on a pilot well and then adjust the model segment by segment using forward modeling constraints from logging data. However, this process is time-consuming and requires numerous repetitive forward modeling calculations for different wells in the same area. Therefore, in the processing and interpretation of horizontal well logging data, developing a reasonable well-formation model is essential. This model enables an accurate description of the spatial relationship between the wellbore and the formation interfaces, including the distance between the wellbore position and formation interfaces and the angle between the wellbore axis and the formation normal direction. At the same time, logging data analysis methods based on machine learning and artificial intelligence (AI) technologies have been applied to various aspects of logging data interpretation by training intelligent models. With the support of AI technologies, it is expected to overcome the bottlenecks of traditional methods. To this end, the study proposed an automated horizontal well logging interpretation method based on multi-model integration and deep neural networks. First, a theoretical model was constructed incorporating different wellbore trajectories and formation combination relationships, and a logging response sample library was generated. Then, machine learning models such as eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were integrated, and their prediction results were further fused using a multi-layer perceptron (MLP). Finally, intelligent automatic recognition of the geometric relationship between the well trajectory and the surrounding rock was carried out using actual logging data. Case analysis showed that this method accurately captured the complex logging response characteristics of horizontal wells while significantly improving interpretation speed and accuracy. The proposed method meets the demand for rapid analysis of multiple wells in similar geological environments and provides an efficient, intelligent approach to horizontal well logging interpretation.