Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network  

作　　者：Zhao-Qin Huang Zhao-Xu Wang Hui-Fang Hu Shi-Ming Zhang Yong-Xing Liang Qi Guo Jun Yao 

机构地区：[1]School of Petroleum Engineering,China University of Petroleum(East China),Qingdao,266580,Shandong,China [2]Exploration and Development Research Institute,Shengli Oilfield,SINOPEC,Dongying,257015,Shandong,China

出　　处：《Petroleum Science》2024年第2期1062-1080,共19页石油科学（英文版）

基　　金：the support of the National Nature Science Foundation of China(No.52074336);Emerging Big Data Projects of Sinopec Corporation(No.20210918084304712)。

摘　　要：The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

关 键 词：Graph neural network Dynamic interwell connectivity Production-injection splitting Attention mechanism Multi-layer reservoir 

分 类 号：TE357.6[石油与天然气工程—油气田开发工程] TP18[自动化与计算机技术—控制理论与控制工程]