用生成模型从稀疏井数据重建地下渗透率与饱和度分布
Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models
- 基于评分生成模型学习多相流在多孔介质中的时空动态
- 仅凭两个井位的稀疏数据即可还原完整空间场
- 适合地质数据少的油气田开发与碳封存场景
本研究探索了评分生成模型在油藏模拟中的应用，重点在于从两个井位的稀疏观测数据中重构盐水含水层中空间变化的渗透率与饱和度场。通过建模高保真油藏模拟所得的渗透率与饱和度联合分布，所提出的神经网络学习了多相流在多孔介质中的复杂时空动力学。推理阶段，该框架通过条件化于井筒测井数据提取的稀疏垂直剖面，有效重建了渗透率与饱和度场。该方法首次将物理约束与测井引导融入生成模型，显著提升重建结果的准确性和物理解释性。此外，框架在不同地质情景下表现出强泛化能力，显示出其在数据稀缺的油藏管理任务中的实际应用潜力。
原文摘要 · Abstract (English)
This study investigates the use of score-based generative models for reservoir simulation, with a focus on reconstructing spatially varying permeability and saturation fields in saline aquifers, inferred from sparse observations at two well locations. By modeling the joint distribution of permeability and saturation derived from high-fidelity reservoir simulations, the proposed neural network is trained to learn the complex spatiotemporal dynamics governing multiphase fluid flow in porous media. During inference, the framework effectively reconstructs both permeability and saturation fields by conditioning on sparse vertical profiles extracted from well log data. This approach introduces a novel methodology for incorporating physical constraints and well log guidance into generative models, significantly enhancing the accuracy and physical plausibility of the reconstructed subsurface states. Furthermore, the framework demonstrates strong generalization capabilities across varying geological scenarios, highlighting its potential for practical deployment in data-scarce reservoir management tasks.
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