Abstract Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and development. A multivariate prediction model of multiple petrophysical data is required to adequately reflect variations in pore pressure. However, the parametric multivariate models with assumptions on lithology, predominantly sand or shale, are theoretically inaccurate for mixed lithologies and require a tedious calibration process. Here, we propose a new method of utilizing machine learning (ML) techniques for pore-pressure prediction with a nonparametric multivariate model of petrophysical properties (sonic velocity, porosity, and shale volume). The training dataset for the ML models is constructed using petrophysical properties extracted from well log data and theoretical effective stress in the normally compacted interval. Bowers’ unloading relation is invoked herein to account for abnormal pressure generated by unloading. Four ML algorithms, including the multilayer perceptron neural network, support vector machine, random forest, and gradient boosting machine, are applied to well log data from a set of offshore exploration wells in the East China Sea Shelf Basin. The results suggest that the proposed method using ML makes pore-pressure predictions in good agreement with pore-pressure measurement, and random forest outperforms the other ML algorithms in terms of goodness-of-fit, generalizability, and prediction accuracy. Compared with methods based on parametric models, the proposed method based on ML produces more accurate pore-pressure prediction and better capture the onset of overpressure.

中文翻译：

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一种用于多元孔隙压力预测的机器学习方法

摘要 准确的孔隙压力预测对油气勘探开发具有重要意义。需要多个岩石物理数据的多变量预测模型来充分反映孔隙压力的变化。然而，假设岩性（主要是砂岩或页岩）的参数多元模型对于混合岩性在理论上是不准确的，并且需要繁琐的校准过程。在这里，我们提出了一种利用机器学习 (ML) 技术通过岩石物理特性（声速、孔隙度和页岩体积）的非参数多元模型进行孔隙压力预测的新方法。ML 模型的训练数据集是使用从测井数据中提取的岩石物理特性和正常压实层段中的理论有效应力构建的。这里引用 Bowers 卸载关系来解释卸载产生的异常压力。将多层感知器神经网络、支持向量机、随机森林和梯度提升机等四种ML算法应用于东海陆架盆地一组海上勘探井的测井数据。结果表明，所提出的使用 ML 的方法使孔隙压力预测与孔隙压力测量非常吻合，并且随机森林在拟合优度、泛化性和预测精度方面优于其他 ML 算法。与基于参数模型的方法相比，所提出的基于 ML 的方法产生更准确的孔隙压力预测并更好地捕获超压的开始。