Regression-based finite element machines for reliability modeling of downhole safety valves. (June 2020)

• Record Type: Journal Article
• Authors: Colombo, Danilo; Lima, Gilson Brito Alves; Pereira, Danillo Roberto; Papa, João P.
• Is Part Of: Reliability engineering & system safety. Volume 198(2020)
• Publication Date: 2020-06
• Subjects: Reliability prediction -- Safety valve -- Finite element machines; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.106894
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: To introduce Finite Element Machines for Regression (FEMaR) in the context of reliability estimation. To perform reliability estimation of downhole safety valves. To fine-tune Weibull parameters using meta-heuristics. Abstract: Downhole Safety Valve (DHSV) stands for a device widely used in offshore wells to ensure the integrity and avoid uncontrolled leaks of oil and gas to the environment, known as blowouts. The reliability estimation of such valves can be used to predict the blowout occurrence and to evaluate the workover demand, as well as to assist decision-making actions. In this paper, we introduce FEMaR, a Finite Element Machine for regression problems, which figures no training step, besides being parameterless. Another main contribution of this work is to evaluate several machine learning models to estimate the reliability of DHSVs for further comparison against traditional statistical methods. The experimental evaluation over a dataset collected from a Brazilian oil and gas company showed that machine learning techniques are capable of obtaining promising results, even in the presence of censored information, and they can outperform the statistical approaches considered in this work. Such findings also investigated using uncertainty analysis, evidenced that we can save economic resources and increase the safety at the offshore well operations.