为了释放大型相对渗透率 ( K r ) 数据库的潜力，整篇论文中提出的工作流程集成了数据分析、机器学习 (ML) 和人工智能 (AI)。该工作流程允许自动生成干净的数据库和K r数据的数字孪生，使用人工智能通过将岩石分类方案扩展到同一地层的多个油田来识别附近油田的模拟数据。
准确的K r曲线至关重要，因为它们可以改善油藏特征，减少历史匹配和产量预测的不确定性，并提供稳健可靠的油田开发计划。然而，准备特殊岩心分析 (SCAL) 数据（特别是K r曲线）作为油藏模拟的输入传统上是一个高度手动、耗时且劳动密集型的过程。此外，目前还没有可用的自动化工具可以使用分析或数值方法对K r数据进行全面的质量审查。因此，油藏模拟模型中使用的K r数据的质量和代表性经常得不到充分检查。
To unlock the potential of large relative permeability (Kr) databases, the work flow proposed in the complete paper integrates data analysis, machine learning (ML), and artificial intelligence (AI). The work flow allows for the automated generation of a clean database and a digital twin of Kr data, using AI to identify analog data from nearby fields by extending the rock-typing scheme across multiple fields for the same formation.
Accurate Kr curves are critical because they can improve reservoir characterization, reduce uncertainty in history matching and production forecasting, and provide robust and reliable field development plans. However, preparing special core analysis (SCAL) data, particularly Kr curves, as an input for reservoir simulation traditionally has been a highly manual, time-consuming, labor-intensive process. Furthermore, no automated tools are available currently that perform a full quality review of Kr data using analytical or numerical methods. As a result, the quality and representativeness of Kr data used in reservoir simulation models is frequently inadequately checked.
