Self adaptive learning scheme for Fault prognosis in oil wells and production & service lines

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Published Oct 28, 2022
Aymen Harrouz Houari Toubakh Redouane Kafi Moamar Sayed-Mouchaweh Hajer Salem

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

Harrouz, A., Toubakh, H., Kafi, R., Sayed-Mouchaweh, . M., & Salem, H. (2022). Self adaptive learning scheme for Fault prognosis in oil wells and production & service lines. Annual Conference of the PHM Society, 14(1). https://doi.org/10.36001/phmconf.2022.v14i1.3227
Abstract 56 | PDF Downloads 41

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Keywords

Fault Prognosis, Oil & gas wells and lines, Downhole safety valve, Data-Driven, ANN

Section
Technical Papers