Identifying fluid flow maldistribution in planar geometries is a well‐established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two‐dimensional Hele‐Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L² norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C‐Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir‐scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding‐plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

中文翻译：

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短路断裂中预测传热的预测逆模型：尺寸分析，机器学习和现场演示

识别平面几何形状中的流体流动分布不均是地下科学/工程领域公认的问题。对于增强型（或“工程化”）地热系统的热性能而言，尤为重要的是确定不均匀（即非均质）渗透率的存在并随后预测对流换热。在这里，通过遗传算法（GA）进行的机器学习可以识别二维Hele-Shaw几何体（即，平行板）中未知渗透率场的空间分布。通过最小化L ²来解决反问题停留时间分布（RTD）与惰性示踪剂穿透曲线（BTC）（C-Dot纳米粒子）测量之间的标准。空间相关渗透率场的主成分分析（PCA）使参数空间减少了10倍以上，并且将反搜索限制在储层尺度的渗透率变化中。在中等规模的阿尔托纳油田实验室（AFL）进行的热实验和示踪剂测试表明，该方法可准确预测极端流道对单个顺层岩石裂缝中传热的影响。但是，只有当渗透率分布与示踪剂RTD和摩擦压力损失都具有足够的匹配时，这才是正确的。在没有与摩擦压力损失达成良好协议的情况下，仍然有可能将模拟的RTD与测量值进行匹配，但随后对传热的预测非常不准确。这项研究的结果表明，可以预测流量分布不均的热效应，但前提是模拟的RTD和流体入口与出口之间的摩擦压力损失与测量值都非常吻合。