摘要：

地层压力预测是钻井工程中的关键环节，其预测精度直接影响井控安全、钻井风险管理及钻井液密度窗口的合理确定。传统地层压力预测方法主要依赖测井或地震资料进行单一数据源建模，在复杂构造或异常高压发育区，往往难以准确刻画地层压力的突变位置与变化幅度，导致预测结果存在不确定性。以南海乐东工区的黄流组为例，该区地层发育明显的异常高压，其地震波阻抗和岩性变化与压力突变具有较强对应关系，而钻井液密度调整等工程响应所指示的异常高压响应深度通常浅于地震或测井所指示的物理突变位置。该现象揭示了物理信息与工程信息在地层压力异常识别中具有明显互补性。本文提出一种融合物理与工程先验的智能地层压力预测方法，通过将地震反演得到的物理突变先验与工程井控先验共同嵌入深度学习模型，实现目标井钻前地层压力预测。该方法在保证预测压力整体数值精度的同时，有效增强了对地层压力突变特征的刻画能力。实际资料应用结果表明，该方法相比传统预测手段能够显著提高地层压力预测精度，并在关键层段风险识别与安全窗口确定方面表现出更高的可靠性和工程适用性。

关键词: 地层压力预测, 工程先验, 物理先验, 异常高压, 井控安全

Abstract:

Formation pressure prediction is a critical task in drilling engineering, as its accuracy directly affects well control safety, drilling risk management, and the determination of appropriate drilling fluid density windows. Conventional formation pressure prediction methods mainly rely on a single data source, such as well logs or seismic data. In complex structural settings or abnormal overpressure zones, they often fail to accurately characterize the location and magnitude of pressure variations, leading to considerable prediction uncertainty. For example, in the Huangliu Formation of the Ledong area in the South China Sea, abnormal overpressure is developed, and the associated pressure variations exhibit strong correspondence with seismic impedance and lithological changes. Meanwhile, engineering responses, such as drilling fluid density adjustments, usually indicate abnormal overpressure at shallower depths than the physical variations inferred from seismic or logging data. This phenomenon indicates that engineering information and physical information are complementary in identifying formation pressure anomalies. This study proposes an intelligent formation pressure prediction method that integrates physical and engineering priors. Physical priors derived from seismic inversion and engineering well-control priors are jointly embedded into a deep learning model to achieve accurate pre-drilling pressure prediction for target wells. Application results from field data show that the proposed method improves formation pressure prediction accuracy while effectively capturing pressure variation characteristics. It also provides higher reliability in identifying high-risk intervals and determining pressure safety windows in key formations.

Key words: formation pressure prediction, engineering priors, physical priors, abnormal overpressure, well-control safety

中图分类号: 

• P618.13

• TE857.24