Seismic data have the advantage of wide aerial distribution and deep extent unlike well data that are restricted to a borehole’s location, measuring intervals, and depth. In addition, seismic data are suitable for delineation of structural and stratigraphic features, whereas well logs are suitable for delineation of much smaller scale petrophysical properties. However, both methods are beneficial for extending small-scale petrophysical parameters to large-scale seismic volumes. In seismic data, seismic traces are mainly band-limited, whereas sources of seismic data do not offer the entire band of frequencies required for the desired resolution to be comparable to well data. Therefore, it is a big challenge to compare well data with a resolution that is several orders greater than that of seismic data. The integration of petrophysical parameters and seismic traces helps to predict the lateral distribution of petrophysical properties. However, the traditional prediction methods have their limitations. Therefore, this study used seismic and well logs to predict shale volume using the proposed model with an artificial neural network. The proposed hybrid model consists of a conventional random vector functional link (RVFL) network and the transient search optimization (TSO) algorithm, and the model is named TSO–RVFL. This model predicts shale volume in wells. The TSO–RVFL was compared with the standalone RVFL and other two hybrid models. The results of this study validated the successful performance of the artificial neural network for calculating and predicting petrophysical parameters, such as shale volume. The TSO–RVFL outperformed the three other models based on different statistical measures.

与仅限于钻孔位置、测量间隔和深度的井数据不同，地震数据具有广泛的空中分布和深度范围的优势。此外，地震数据适用于描绘构造和地层特征，而测井资料适用于描绘小得多的岩石物理特性。然而，这两种方法都有利于将小尺度岩石物理参数扩展到大尺度地震体。在地震数据中，地震轨迹主要是带限的，而地震数据源不能提供所需分辨率与井数据相当所需的整个频带。因此，比较分辨率比地震数据高几个数量级的井数据是一个很大的挑战。岩石物性参数与地震道的整合有助于预测岩石物性的横向分布。然而，传统的预测方法有其局限性。因此，本研究使用地震和测井记录来预测页岩体积，使用所提出的模型和人工神经网络。所提出的混合模型由传统的随机向量功能链接（RVFL）网络和瞬态搜索优化（TSO）算法组成，该模型被命名为TSO-RVFL。该模型预测井中的页岩体积。TSO-RVFL 与独立的 RVFL 和其他两种混合模型进行了比较。本研究结果验证了人工神经网络在计算和预测页岩体积等岩石物理参数方面的成功性能。