SPE Drilling & Completion
Volume 27,
Number 4,
December 2012,
pp. 586-595
Summary
Differential pipe sticking (DPS) is one of the most conventional and serious
problems in drilling operations that imposes some extra costs to companies.
This phenomenon originates mainly from improper mud properties, bottomhole
assembly (BHA) (contacting area), still pipe time, and differential pressure
between the formation and the drilling mud. Investigation on various conditions
that lead to DPS makes it possible to develop some preventive treatments to
avoid this problem's occurrence. In the past, statistical methods were applied
in this area, but recently artificial neural network (ANN) approaches are
frequently being used. ANNs have some priorities over conventional statistical
methods such as the model-free form of predictions, tolerance to data errors,
data-driven nature, and fast computation. On the other hand, the designed ANNs
have some shortcomings and restrictions as they are developed to predict
problems. In this paper, to solve most of the existing disadvantages of ANNs, a
novel support-vector machine (SVM) approach has been developed to predict a DPS
occurrence in horizontal and sidetracked wells in Iranian offshore oil fields.
The results from the analysis have shown the potential of the SVM and ANNs to
predict DPS, with the SVM results being more promising.
© 2012. Society of Petroleum Engineers
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History
- Original manuscript received:
4 September 2011
- Revised manuscript received:
11 March 2012
- Manuscript approved:
31 July 2012
- Published online:
21 November 2012
- Version of record:
11 December 2012
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