Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

中文翻译：

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基于人工神经网络（ANN-）的代用模型，用于蒸汽驱油藏的快速性能预测和反调度设计

蒸汽驱是重油开发中最有效，最成熟的技术之一。本文首先建立了基于有限体积法的蒸汽驱油藏数值模拟技术。结合汽相的相变，采用自适应时步牛顿迭代法进行蒸汽驱的全隐式求解。考虑储层非均质性，采用克里格法随机生成4250个地质模型样本，并随机给出相应的生产进度参数。然后，通过数值模拟技术处理这些储层模型样本，以获得相应的动态生产数据，这些数据构成用于人工神经网络（ANN）训练的数据。利用人工神经网络的高度非线性全局效应及其强大的自适应和自学习功能，建立了蒸汽驱油藏的前瞻性和逆向设计ANN模型，为蒸汽的快速预测提供了一种新方法。注水生产性能和生产进度参数设计。在4250个样本中，前瞻性模型的误差基本上小于0.1％，逆向设计模型的误差通常小于15％。充分表明，本文开发的人工神经网络模型能够快速有效地预测采油量和设计生产参数，对实施蒸汽驱技术具有重要的指导作用。最后，应用前瞻性人工神经网络模型有效地分析了蒸汽驱过程的影响因素，并通过蒙特卡洛模拟对逆向设计人工神经网络模型进行了不确定性分析，以说明其鲁棒性。此外，本文还可为神经网络模型在典型的不可见黑匣子地下油气藏中的应用提供参考。