01950nab a2200205 4500 20260520001757.0 260224s2009 xxu Casing collapse risk assessment and depth prediction with a neural network system approach nov. 2009 16/06/2010 ; 16/06/2010 7 p. ; 156-162 Transcripción 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. 1-2 Journal of Petroleum Science & Engineering 69 ARTICULO Salehi, Saeed 42893 Hareland, Geir 10286 Ganji, Mehdi 42894 171133 171133 0 0 0 0 238952 BAAB BAAB 2026-03-05 200045991 0 200045991 2026-03-05 15:13:30 2026-03-05 ARTICULO