ZHU Xuan,YUAN Bin,TONG Yuanhui,et al.Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs[J].Journal of Shenzhen University Science and Engineering,2022,39(5):559-566.[doi:10.3724/SP.J.1249.2022.05559]





Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs
朱璇1 袁彬1 同元辉2 赵明泽1 郑贺1 刘秀磊1
1)中国石油大学(华东)石油工程学院,山东青岛 266580
ZHU Xuan1 YUAN Bin1 TONG Yuanhui2 ZHAO Mingze1 ZHENG He1 and LIU Xiulei1
1) School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong Province, P.R.China
2) Center of Oil & Gas Analysis, PetroChina Tarim Oilfield Experimental Testing Institute, Korla 841009, Xinjiang Uygur Autonomous Region, P.R.China
oilfield development tight oil reservoir gas flooding fracture well pattern deep learning proxy model enhanced oil recovery horizontal well
压裂井网气驱将油藏压裂与注气井网驱油结合,是当前致密低渗油藏提高采收率有效技术之一.水力裂缝及多相流动复杂性,使得基于精细油藏数值模拟的压裂井网气驱效果预测变得困难且耗时.提出一种基于均方根传播(root mean square propagation, RMSProp)深度学习的压裂井网气驱效果预测方法.通过建立压裂直井/水平井混合井网气驱数值模拟模型,引入高斯函数定量表征压裂水平井多级裂缝分布特征.利用正交试验筛选试验样本方案,自主编程实现数值模拟结果自动提取与数据处理,建立致密低渗油藏压裂井网气驱样本数据库.基于随机森林算法,筛选油藏地质、裂缝、生产等关键参数重要性特征,通过误差逆传播(back propagation, BP)神经网络、长短期记忆单元(long short-term memory, LSTM)、双向长短期记忆单元(bi-directional long short-term memory, BiLSTM)等深度学习算法,建立日产油、地层压力和采出程度预测代理模型,通过与油藏数值模拟对比,验证模型准确性.结果表明,BiLSTM算法在预测压裂井网气驱和压裂衰竭开发时效果最好.所提出的基于RMSProp的深度学习方法有效兼顾了模型实用性与精确性,为致密低渗油藏压裂井网气驱模拟预测提供了新途径.
Gas flooding in fractured well pattern is a combination of hydraulic fracturing and gas injection to displace reservoir oil within well pattern, and is one of the effective technologies for improving oil recovery in tight and low permeability reservoirs. However, due to the complexity of hydraulic fracturing and multi-phase flow, it becomes difficult and time-consuming to establish a fine reservoir numerical simulation to predict the performance of gas flooding in fractured well pattern. Therefore, we propose a deep learning method based on root mean square back propagation (RMSProp) for predicting the gas flooding effect of fracturing well pattern. Firstly, we establish a basic numerical simulation model of gas flooding in fractured vertical/horizontal hybrid well pattern in tight oil reservoir. Gaussian function curve is introduced to quantify the position of heel/toe of horizontal well and distributions of hydraulic fracture tips. The orthogonal test is then used to screen the test sample set, and the simulation results are auto-extracted and processed to build a database of gas flooding in tight oil reservoirs. The key parameters such as reservoir geology, fracture and production as input features are screened according to their importance in prediction by using the random forest algorithm. Then, we establish the prediction proxy models of daily oil production, reservoir pressure and field oil extraction (FOE) through deep learning algorithms such as back propagation (BP) neural network, long short-term memory (LSTM) and bi-directional long-short term memory (BiLSTM). Compared with the results of reservoir numerical simulation, the accuracy of the prediction proxy model is verified, and the results proves that the BiLSTM algorithm has the best performance in forecasting the gas flooding performance of fractured well pattern. The RMSprop-based deep learning method effectively takes into account the practicability and accuracy of the model andprovides a new approach to reservoir simulation and prediction of the fracturing well pattern gas flooding in tight and low permeability reservoirs.


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Received: 2022- 04-22; Accepted: 2022-06-27; Online (CNKI): 2022-08-09
Foundation: National Natural Science Foundation of China (52074338);National Key R & D Program of China (2019YFA0708700); Shandong Mountain Tai Scholar Program (ZX20210178)
Corresponding author: Professor YUAN Bin. E-mail: yuanbin@upc.edu.cn
Citation: ZHU Xuan, YUAN Bin, TONG Yuanhui, et al. Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(5): 559-566.(in Chinese)
作者简介:朱璇(1999—),中国石油大学(华东)硕士研究生.研究方向:油气田开发工程. E-mail: s21020110@s.upc.edu.cn
袁彬(1988—),中国石油大学(华东)教授、博士生导师.研究方向:渗流理论与油气藏开发工程、油气工程信息与智能技术. E-mail: yuanbin@upc.edu.cn
更新日期/Last Update: 2022-09-30