Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)

作     者：Areeba Ansari Marco Heras Julianne Nones Mehdi Mohammadpoor Farshid Torabi 

作者机构：Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSaskatchewanS4S 0A2Canada 

出 版 物：《Petroleum》 (油气（英文）)

年 卷 期：2020年第6卷第4期

页      面：368-374页

核心收录：

学科分类：0820[工学-石油与天然气工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 082002[工学-油气田开发工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Canada Mitacs, Canada Petroleum Systems Engineering department University of Regina Petroleum Technology Research Centre, PTRC 

主　　题：Enhanced oil recovery Steam assisted gravity drainage Artificial neural network 

摘      要：As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective *** problem is especially apparent in Western Canada,where most oil production is dependent on costly enhanced oil recovery(EOR)techniques such as steam-assisted gravity drainage(SAGD).Therefore,the goal of this study is to create an artificial neural network(ANN)that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage(SAGD).The developed ANN model featured over 250 unique entries for oil viscosity,steam injection rate,horizontal permeability,permeability ratio,porosity,reservoir thickness,and steam injection pressure collected from *** collected data set was entered through a feed-forward back-propagation neural network to train,validate,and test the model to predict the recovery factor of SAGD method as accurate as *** from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10%*** the neural network was exposed to a new simulation data set of 64 points,the predictions were found to have an accuracy of 82%as measured by linear ***,the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.