Increasing world's oil demand is pushing exploration to considering also deep waters. This implies the drilling of a salt layer, with consequent formation of lime scale on the materials of the oil wells plants. Lime scale can cause the blocking of valves, tubing and flowlines, bringing unscheduled shutdowns and interruption of production. Therefore, the prediction of lime scale deposition under different chemical and operating conditions is fundamental for maintenance planning. To this aim, experimental tests have been conducted to study lime scale deposition on the materials used in off-shore drilling. In this work, these data have been employed to develop an ensemble of neural networks for the prediction of scale deposition in oil well plant equipment. Copyright © (2012) by IAPSAM & ESRA.
Ensemble of neural networks for predicting scale deposition in oil well plant equipments
Baraldi P.;Di Maio F.;Zio E.;
2012-01-01
Abstract
Increasing world's oil demand is pushing exploration to considering also deep waters. This implies the drilling of a salt layer, with consequent formation of lime scale on the materials of the oil wells plants. Lime scale can cause the blocking of valves, tubing and flowlines, bringing unscheduled shutdowns and interruption of production. Therefore, the prediction of lime scale deposition under different chemical and operating conditions is fundamental for maintenance planning. To this aim, experimental tests have been conducted to study lime scale deposition on the materials used in off-shore drilling. In this work, these data have been employed to develop an ensemble of neural networks for the prediction of scale deposition in oil well plant equipment. Copyright © (2012) by IAPSAM & ESRA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
