Pore pressure prediction is fundamental when drilling deep and geologically complex reservoirs. Even in relatively well-characterized hydrocarbon reservoir fields, with a considerable number of drilled wells, when located in challenging geological environments, poor prediction of abnormal pore pressure might result in catastrophic events that can cause harm to human lives and infrastructures. To better quantify drilling risks, the uncertainty associated with the pore pressure prediction should be integrated within the geo-modelling workflow. Leveraging a challenging real case from the Brazilian pre-salt, the work presented herein proposes a seismic-driven gradient pore pressure modelling workflow, which combines machine learning and geostatistical co-simulation to predict high-resolution gradient pore pressure volumes. First, existing angle-dependent seismic reflection data are inverted for P- and S-wave velocity and density. Then, K-nearest neighbor is used to create a regression model between pore pressure gradient and P- and S-wave velocity, density and depth based on the well log information. The trained model is applied to predict a three-dimensional gradient pore pressure model from the models obtained from geostatistical seismic inversion. This gradient pore pressure model is a smooth representation of the highly variable subsurface and is used as secondary variable in stochastic sequential co-simulation with joint probability distributions to generate multiple high-resolution realizations of gradient pore pressure. The ensemble of co-simulated models can be used to assess the spatial uncertainty about the gradient pore pressure predictions. The results of the application example show the ability of the method to reproduce the spatial patterns observed in the seismic data and to reproduce existing gradient pore pressure well logs at two blind well locations, which were not used to condition the gradient pore pressure predictions.

孔隙压力预测对于钻探深层且地质复杂的储层至关重要。即使在具有相当数量钻井的特征相对较好的油气藏领域，当位于具有挑战性的地质环境中时，对异常孔隙压力的预测不佳也可能导致灾难性事件，从而对人类生命和基础设施造成损害。为了更好地量化钻井风险，与孔隙压力预测相关的不确定性应集成到地质建模工作流程中。本文利用巴西盐下具有挑战性的真实案例，提出了一种地震驱动的梯度孔隙压力建模工作流程，该工作流程结合了机器学习和地质统计协同模拟来预测高分辨率梯度孔隙压力体积。首先，对现有的角度相关地震反射数据进行纵波和横波速度和密度反演。然后，根据测井信息，使用K最近邻建立孔隙压力梯度与纵波和横波速度、密度和深度之间的回归模型。训练后的模型用于根据地质统计地震反演获得的模型来预测三维梯度孔隙压力模型。该梯度孔隙压力模型是高度变化的地下的平滑表示，并在具有联合概率分布的随机顺序联合模拟中用作次要变量，以生成梯度孔隙压力的多个高分辨率实现。联合模拟模型的集合可用于评估梯度孔隙压力预测的空间不确定性。应用示例的结果表明，该方法能够重现在地震数据中观察到的空间模式，并重现两个盲井位置处现有的梯度孔隙压力测井曲线，这些盲井位置不用于条件梯度孔隙压力预测。