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A steam injection distribution optimization method for SAGD oil field using LSTM and dynamic programming
ISA Transactions ( IF 7.3 ) Pub Date : 2020-10-15 , DOI: 10.1016/j.isatra.2020.10.029
Changlin Yang , Xin Wang

Steam injection distribution optimization refers to the process of distributing steam injection in steam assisted gravity drainage (SAGD) oil field to maximize the total oil production. A novel optimization method that integrates long short-term memory (LSTM) neural network and dynamic programming is presented in this paper to solve the steam injection distribution optimization problem for the first time. In the proposed method, LSTM is used to construct the prediction model to predict oil production of the wells. With the prediction result, dynamic programming optimizes steam injection distribution in the oil field to maximize total oil production. Convergence stability and computational complexity of the dynamic programming method have been analyzed and presented in this research. A web-based geographical information system called Petroleum Explorer is also developed based on the proposed method. Experiments on two pads of a real-world SAGD project demonstrate that LSTM model gives better prediction result than other five existing models and production improvement of the proposed method is highly related to parameter setting of the optimization process



中文翻译:

基于LSTM和动态规划的SAGD油田注汽分配优化方法。

蒸汽注入分配优化是指在蒸汽辅助重力排水(SAGD)油田中分配蒸汽注入以最大程度提高总采油量的过程。提出了一种结合长短期记忆(LSTM)神经网络和动态规划的优化方法,首次解决了注汽分布优化问题。在所提出的方法中,LSTM用于构建预测模型以预测井的产油量。利用预测结果,动态编程可以优化油田中的蒸汽注入分布,以最大程度地提高石油总产量。分析并提出了动态规划方法的收敛稳定性和计算复杂度。基于提出的方法,还开发了基于Web的地理信息系统,称为Petroleum Explorer。在一个实际的SAGD项目的两个平台上进行的实验表明,LSTM模型比其他五个现有模型提供了更好的预测结果,并且该方法的生产改进与优化过程的参数设置高度相关

更新日期:2020-10-15
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