New method for prediction and solving the problem of drilling fluid loss using modular neural network and particle swarm optimization algorithm

被引:23
作者
Toreifi, Hojjat [1 ]
Rostami, Habib [2 ]
Manshad, Abbas Khaksar [3 ]
机构
[1] Persian Gulf Univ, Sch Engn, Oil Engn Dept, Bushehr, Iran
[2] Persian Gulf Univ, Sch Engn, Comp Engn Dept, Bushehr, Iran
[3] Petr Univ Technol, Abadan Fac Petr Engn, Dept Petr Engn, Abadan, Iran
关键词
Loss circulation; Modular neural network; Loss circulation reduction; Particle swarm optimization algorithm;
D O I
10.1007/s13202-014-0102-5
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial intelligence (Al) is a new method of solving engineering problems that has the ability to consider all effective parameters simultaneously. Moreover, it has generalization and the ability to learn directly from field data. In this paper, two models were designed using Al and data of 38 wells located in Maroun oil field. Both models were developed by modular neural network, to predict loss circulation in quality and quantity. Then, the particle swarm optimization algorithm was used to minimize loss circulation. The accuracy of two models in predicting loss circulation quantitatively and qualitatively is 0.94 and 0.98 %, respectively.
引用
收藏
页码:371 / 379
页数:9
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