With the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R² being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.

借助机器学习方法，即人工神经网络，我们建立了数据驱动的代理模型，该模型可用于通过在生产期间调整井控注入率来最大化注水过程的净现值。这些数据驱动的代理在两个不同的案例研究中进行了操作，其中包括合成 2D 油藏模型和 3D 油藏模型（Egg 模型）。在算法方面，我们应用了两种不同的自然启发式元启发式算法，即粒子群优化和灰狼优化来执行优化任务。关于代理模型的开发，我们证明了训练和盲验证的结果非常好（决定系数，R ²约为 0.99）。对于案例研究和所采用的优化算法，与油藏模拟器相比，使用代理模型获得的优化结果都在 5% 以内（满意的精度水平）。这些结果证实了该方法在开发代理模型中的有用性。除此之外，使用代理显着降低了优化的计算成本。这进一步突出了在实际使用中采用代理模型的显着优势，尽管受到一些限制。