000			01950nab a2200205 4500
005			20260520001757.0
008			260224s2009 xxu
245	0	0	_aCasing collapse risk assessment and depth prediction with a neural network system approach
260			_a _b _cnov. 2009
270			_a16/06/2010 ; 16/06/2010
300			_a7 p. ; 156-162
520			_aTranscripción del resumen del autor. A large carbonate oil field in Iran is suffering from severe casing collapses. 48 casing collapses have been occurred due to reservoir compaction, poro-elastic effects and corrosion. The application of neural networks for predicting casing collapses using complex multi-dimensional field data has been undertaken. This paper shows how a neural network (ANN) system can be trained based on the parameters affecting casing collapse to estimate the potential of collapse of wells to be drilled as well as the current wells producing in the field. The potential use of this type of analysis is large in that it can be linked as a critical risking parameter in future field development analysis. Being able to quantify the potential for collapse of a well in the future can give management the foundation for a better financial decision making on what wells and where to drill them with the potential for the larger net return on the investment. The estimated collapse and corresponding depth could also benefit in the type of casing design and completion method to be selected as well as workover designs. Interpretation of the neural network results, together with engineering judgment, allowed us to conclude that using this method is technically feasible for predicting casing collapses in this field.
581			_a1-2
773	0		_tJournal of Petroleum Science & Engineering _g69
942			_cARTICULO
100	1		_aSalehi, Saeed _942893
100	1		_aHareland, Geir _910286
100	1		_aGanji, Mehdi _942894
999			_c171133 _d171133