Characterizing reservoir porosity is crucial for oil and gas exploration and reservoir evaluation. Due to the increasing demands of oil and gas exploration and development, characterizing reservoir porosity to the required precision using current methods is challenging. Therefore, this study proposes a Pearson correlation–random forest (RF) scheme to select optimal seismic attributes for predicting reservoir porosity and a one-dimensional convolutional neural network–gated recurrent unit (1D CNN–GRU) joint model for reservoir porosity prediction based on well logs and seismic attribute data. First, Pearson correlation–RF is used to select the optimal combination of seismic attribute data suitable for network training. The model learns the nonlinear mapping between porosity logs at well sites and seismic attribute data. It can precisely predict three-dimensional porosity volumes by extending these mappings to nonwell areas. By performing tests near a tight sandstone reservoir, the predicted porosities of the proposed 1D CNN–GRU joint model were a better fit for true porosity values than those of single-network models. Furthermore, the proposed model obtained a laterally contiguous description of the shape and porosity distribution of the tight sandstone reservoir. By integrating advanced machine learning techniques with seismic data analysis, this method provides new approaches and ideas for wide-area porosity predictions for tight sandstone reservoirs using seismic data and opens up possibilities for more detailed and accurate subsurface mapping.

表征储层孔隙度对于油气勘探和储层评价至关重要。由于石油和天然气勘探和开发的需求不断增加，使用现有方法表征储层孔隙度以达到所需的精度具有挑战性。因此，本研究提出了一种皮尔逊相关随机森林（RF）方案来选择最佳地震属性来预测储层孔隙度，并提出了一种基于一维卷积神经网络门控循环单元（1D CNN-GRU）联合模型来预测储层孔隙度测井曲线和地震属性数据。首先，利用Pearson相关-RF来选择适合网络训练的地震属性数据的最佳组合。该模型学习井场孔隙度测井与地震属性数据之间的非线性映射。它可以通过将这些映射扩展到非井区域来精确预测三维孔隙度体积。通过在致密砂岩储层附近进行测试，所提出的一维 CNN-GRU 联合模型的预测孔隙度比单网络模型更适合真实孔隙度值。此外，所提出的模型获得了致密砂岩储层的形状和孔隙度分布的横向连续描述。通过将先进的机器学习技术与地震数据分析相结合，该方法为利用地震数据进行致密砂岩储层的大面积孔隙度预测提供了新的方法和思路，并为更详细、更准确的地下测绘开辟了可能性。