Pore pressure (P_P) is the most significant and dynamic parameter in reservoir geomechanics, and it optimizes well drilling in the hydrocarbon industry. Improved error accuracy for P_P prediction could reduce drilling risk and hazards, and improve wellbore stability and better casing seat selection. Choosing the appropriate mud weight design for an optimized wellbore drilling is another aspect of P_P prediction. Initial estimates of the vertical stress (S_V) are made in the petrophysical log (especially sonic, density, and resistivity). We attempted to predict P_P using four empirical models: the Eaton, Bower, Miller, and Tau models. The magnitudes of S_V and P_P ranged 25.87–32.72 MPa and 25.31–31.82 MPa, respectively, in the depth interval of 2548.12–2980.02 m, respectively, for linked wells at site National Gas Hydrate Program (NGHP) Expedition-02. In contrast, logging while drilling (LWD) derived actual and predicted pressures were validated with coefficients of determination, R², varying from 0.995 to 0.998, which were used to evaluate the most precise P_P prediction. Further, robust machine learning (ML) techniques, namely artificial neural networks (ANN), decision trees (DT), and support vector regression (SVR), were employed for the prediction of P_P using petrophysical log datasets. As a result, numerous datasets were collected from selected wells and applied for model training, testing, and validation. The DT (best-suited) techniques produced the most accurate prediction for P_P, with R² of 0.998. No overpressure generation, whereas normal pressure was monitored in the gas hydrate zone, and slightly higher pressure was experienced in the free gas zone.

孔隙压力 ( P _P ) 是储层地质力学中最重要的动态参数，它可优化碳氢化合物行业的钻井。提高P _P预测的误差准确性可以降低钻井风险和危害，并提高井眼稳定性和更好的套管座选择。为优化井眼钻井选择合适的泥浆比重设计是P _P预测的另一个方面。垂直应力 ( S _V ) 的初步估计是在岩石物理测井（尤其是声波、密度和电阻率）中进行的。我们尝试使用四种经验模型来预测P _P：Eaton、Bower、Miller 和 Tau 模型。国家天然气水合物计划 (NGHP) Expedition-02 现场连井深度为 2548.12~2980.02 m，S V 和 P P 大小分别为 25.87~32.72 MPa和_25.31 ~ 31.82 _MPa。相比之下，随钻测井 (LWD) 得出的实际压力和预测压力通过从 0.995 到 0.998 变化的确定系数R ²进行验证，该系数用于评估最精确的P _P预测。此外，鲁棒的机器学习 (ML) 技术，即人工神经网络 (ANN)、决策树 (DT) 和支持向量回归 (SVR)，被用于使用石油物理测井数据集预测P _P。因此，从选定的井中收集了大量数据集并应用于模型训练、测试和验证。DT（最适合）技术对P _P进行了最准确的预测，R ²为 0.998。没有超压产生，而天然气水合物区监测到压力正常，游离气区压力稍高。