结合机理与机器学习的、与试井测量结果一致的油藏渗透率图构建方法
Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements
AI总结：
本研究提出一种结合Nadaraya-Watson核回归与神经网络代理模型的油藏渗透率图构建方法，可匹配测井与试井数据，在Egg Model上验证了其水动力学相似性与生产模拟匹配效果。
摘要：
我们提出了一种构建与油藏测井及试井测量解释结果一致的绝对渗透率图的新方法。该方法采用Nadaraya-Watson核回归来近似岩石渗透率的二维空间分布。核回归的参数通过求解优化问题进行调优：对于油藏中的每一口井，我们最小化实际值与预测值之间的差异，涉及三个指标：（i）井位处的绝对渗透率（来自测井）；（ii）井周区域的绝对积分渗透率；（iii）表皮系数（来自试井）。\n反问题通过多次求解正问题来解决，其中我们利用代理模型估算井周油藏的积分渗透率和表皮系数。该代理模型基于人工神经网络开发，训练所用的物理驱动合成数据集通过以下流程生成：先在油藏模拟器中对井底压力下降曲线进行数值模拟，再使用半解析油藏模型对其进行解释。\n我们将所开发的油藏渗透率图构建方法应用于已有的Egg Model（鸡蛋模型）——该模型因存在高渗通道而具有高度非均质的渗透率分布。结果表明，构建的渗透率图与原始渗透率图具有水动力学相似性：在模拟周期结束时，采用构建渗透率图和原始渗透率图的油藏生产数值模拟，在孔隙压力和流体饱和度分布方面定量相似；此外，我们还发现两者的数值模拟结果在流量，以及产油量、产水量、注水量的总体积方面匹配度良好。
英文摘要：
We propose a new method for construction of the absolute permeability map consistent with the interpreted results of well logging and well test measurements in oil reservoirs. Nadaraya-Watson kernel regression is used to approximate two-dimensional spatial distribution of the rock permeability. Parameters of the kernel regression are tuned by solving the optimization problem in which, for each well placed in an oil reservoir, we minimize the difference between the actual and predicted values of (i) absolute permeability at the well location (from well logging); (ii) absolute integral permeability of the domain around the well and (iii) skin factor (from well tests). Inverse problem is solved via multiple solutions to forward problems, in which we estimate the integral permeability of reservoir surrounding a well and the skin factor by the surrogate model. The last one is developed using an artificial neural network trained on the physics-based synthetic dataset generated using the procedure comprising the numerical simulation of bottomhole pressure decline curve in reservoir simulator followed by its interpretation using a semi-analytical reservoir model. The developed method for reservoir permeability map construction is applied to the available reservoir model (Egg Model) with highly heterogeneous permeability distribution due to the presence of highly-permeable channels. We showed that the constructed permeability map is hydrodynamically similar to the original one. Numerical simulations of production in the reservoir with constructed and original permeability maps are quantitatively similar in terms of the pore pressure and fluid saturations distribution at the end of the simulation period. Moreover, we obtained an good match between the obtained results of numerical simulations in terms of the flow rates and total volumes of produced oil, water and injected water.
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