In the realm of oil and gas exploration, accurate identification of lithology is imperative for the assessment of resources and the refinement of extraction strategies. While artificial intelligence techniques have garnered considerable success in lithology identification, existing methodologies encounter difficulties when addressing highly heterogeneous and geologically intricate unconventional oil and gas reservoirs. Specifically, they struggle to account for the dynamic variations in sample characteristics across spatial dimensions and temporal sequences. This separate treatment of spatial and temporal dynamics not only confines the precision of fluid prediction but also significantly attenuates the robustness of the models. To address this challenge, we propose the spatiotemporal network (STNet), a dual-branch deep learning framework that integrates seamlessly spatial feature graph methods with time-sequential prediction methods. By employing a graph structure that accounts for spatial characteristics to capture the complex spatial relationships within logging data, and by utilizing a temporal model to discern the dynamic properties of time series data, this dual-mechanism framework enables a more comprehensive understanding of the multidimensional attributes of subsurface fluids, thereby enhancing the accuracy of lithology identification. Experimental results from multiple wells in different regions of the Tarim and Daqing oilfields demonstrate that STNet not only achieves detection accuracy exceeding 95% but also exhibits strong generalizability. The results indicate a significant improvement in the accuracy of lithology identification compared to seven other advanced models. Integrating both temporal and spatial elements of logging data provides a new perspective for enhancing fluid prediction capabilities.

在油气勘探领域，准确识别岩性对于资源评估和精细化开采策略至关重要。虽然人工智能技术在岩性识别方面取得了相当大的成功，但现有的方法在处理高度非均质且地质复杂的非常规油气藏时遇到了困难。具体来说，他们很难解释样本特征在空间维度和时间序列上的动态变化。这种对空间和时间动力学的单独处理不仅限制了流体预测的精度，而且还显着削弱了模型的鲁棒性。为了应对这一挑战，我们提出了时空网络（STNet），这是一种双分支深度学习框架，它将空间特征图方法与时序预测方法无缝集成。通过采用考虑空间特征的图结构来捕获测井数据中复杂的空间关系，并利用时间模型来识别时间序列数据的动态特性，这种双机制框架可以更全面地理解多维属性从而提高岩性识别的准确性。塔里木油田和大庆油田不同地区多口井的实验结果表明，STNet不仅检测精度超过95%，而且具有很强的泛化性。结果表明，与其他七个先进模型相比，岩性识别的准确性有了显着提高。 集成测井数据的时间和空间元素为增强流体预测能力提供了新的视角。