Prediction of running forces in high curvature well bores using finite element analysis and artificial neural network

A. C. Seibi, A. Gastli, A. Al-Shabibi, H. A. Abdullah

Research output: Contribution to journalArticle

Abstract

Estimation of the vertical force at the kick-off point (k.o.p) is of major concern to field engineers involved in horizontal drilling. Prior knowledge of the level of magnitude of the vertical force assists engineers in selecting appropriate hole paths to be drilled in order to minimize the risk of pipe failure. Current methods employed to approximate the vertical force are based on simple mathematical models that are not necessarily representative of field conditions. This paper presents a new approach based on the use of Artificial Neural Network (ANN) to predict the vertical forces at the k.o.p, which is required to push pipes through curved hole sections. The artificial neural network learns the relationship between field variables and the vertical forces through generated results using a finite element package and offers a quick and efficient way of estimating vertical forces at the k.o.p for various field conditions. The effect of pipe stiffness, hole radius (build-up rate), hole roughness, and the horizontal drag force applied at the end of build (e.o.b) are investigated. The finite element analysis and ANN results showed that the running force variation at the k.o.p increases as the horizontal force, buildup rate and drag increase. The results also showed that the pipe stiffness has negligible effect on the variation of running force at high buildup rate whereas a significant effect is observed at low buildup rate.

Original languageEnglish
Pages (from-to)521-534
Number of pages14
JournalPetroleum Science and Technology
Volume19
Issue number5-6
DOIs
Publication statusPublished - Jun 2001

Fingerprint

artificial neural network
curvature
pipe
Pipe
Neural networks
Finite element method
well
prediction
drag
Drag
stiffness
Stiffness
Horizontal drilling
Engineers
horizontal drilling
roughness
Surface roughness
Mathematical models
analysis
rate

Keywords

  • Artificial Neural Network
  • Build-up rate (dogleg)
  • High curvature well bores
  • Horizontal drilling
  • Vertical force at k.o.p

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Energy Engineering and Power Technology
  • Fuel Technology

Cite this

Prediction of running forces in high curvature well bores using finite element analysis and artificial neural network. / Seibi, A. C.; Gastli, A.; Al-Shabibi, A.; Abdullah, H. A.

In: Petroleum Science and Technology, Vol. 19, No. 5-6, 06.2001, p. 521-534.

Research output: Contribution to journalArticle

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