Self adaptive learning scheme for Fault prognosis in oil wells and production & service lines
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
Fault Prognosis, Oil & gas wells and lines, Downhole safety valve, Data-Driven, ANN
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.