Paper Information

Journal:   IRANIAN JOURNAL OF CHEMICAL ENGINEERING   FALL 2010 , Volume 7 , Number 4; Page(s) 29 To 41.
 
Paper: 

DRILLING STUCK PIPE PREDICTION IN IRANIAN OIL FIELDS: AN ARTIFICIAL NEURAL NETWORK APPROACH

 
 
Author(s):  SHADIZADEH S.R.*, KARIMI F., ZOVEYDAVIANPOUR M.
 
* ABADAN FACULTY OF PETROLEUM ENGINEERING, PETROLEUM UNIVERSITY OF TECHNOLOGY, ABADAN, IRAN
 
Abstract: 

Stuck pipe is one of the most serious drilling problems, estimated to cost the petroleum industry hundreds of millions of dollars annually. One way to avoid stuck pipe risks is to predict the stuck pipe with the available drilling parameters which can be employed to modify drilling variables. In this work, Artificial Neural Network (ANN) was used for stuck pipe prediction according to the fact that this method is applicable when relationships of parameters are too complicated. Based on the drilling fluid condition from one of the Iranian oil fields, stuck pipe instances were divided into static and dynamic types. The results of this study show more than 90% accuracy for stuck pipe prediction in the investigated oilfield. The methodology presented in this paper enables the Iranian drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.

 
Keyword(s): ARTIFICIAL NEURAL NETWORK, STUCK PIPE, IRANIAN OIL FIELD, DIFFERENTIAL STICKING, RISK OF STICKING, STATIC AND DYNAMIC
 
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