Self adaptive learning scheme for Fault prognosis in oil wells and production & service lines
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Abstract
The production of Oil & Gas from underground reservoirs involves chemical and mechanical processes that affect well drilling and operation. Many of these processes may eventually cause a problem with the well, resulting in a decrease in production or in equipment failure. This paper deals with fault prognosis during the practical operation of Oil & Gas wells. This work focus on the remaining useful life prediction of the “Spurious Closure of the Downhole Safety Valve” fault. This paper proposes a scheme based on the use of unsupervised machine learning approach and a drift detection mechanism is employed in order to predict the time to failure, real fault scenarios data are used, the proposed scheme is evaluated using different prognosis performance metrics.
How to Cite
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Fault Prognosis, Oil & gas wells and lines, Downhole safety valve, Data-Driven, ANN
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