Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

中文翻译：

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应用人工神经网络优化非均质油藏注水井位置的数据驱动建模，降低观测成本

二次采油方法如注水通常用于枯竭油藏以提高石油和天然气产量。鉴于在非均质储层的数值模拟中需要大量离散单元，运行多个全物理模拟是不可行的。在这方面，我们提出了一种数据驱动的建模方法，可以有效地预测油气产量，并大大降低此类问题的计算和观测成本。我们通过应用人工神经网络和少量训练数据集来预测流体产量作为非均质性和注入井位置的函数，这些数据集是通过全物理仿真模型获得的。为了提高预测的准确性，我们利用生产井和注入井的数据来实现经济高效的预测，而无需任何储层地质信息。建议的人工神经网络建模方法仅利用井数据，能够以降低的计算和观察成本做出有效的决策。