Incorporation of frailties into a non-proportional hazard regression model and its diagnostics for reliability modeling of downhole safety valves

TL;DR

This paper introduces a frailty-augmented non-proportional hazards regression model with diagnostics, applied to reliability analysis of downhole safety valves in offshore oil and gas production.

Contribution

It develops a novel frailty-based extension of the GTDL model for time-to-event data, incorporating time effects and unobservable heterogeneity.

Findings

Model effectively captures unobserved heterogeneity in DHSV data.

Application demonstrates improved reliability estimation for safety valves.

Provides tools for better decision-making in offshore safety management.

Abstract

In this paper, our proposal consists of incorporating frailty into a statistical methodology for modeling time-to-event data, based on non-proportional hazards regression model. Specifically, we use the generalized time-dependent logistic (GTDL) model with a frailty term introduced in the hazard function to control for unobservable heterogeneity among the sampling units. We also add a regression in the parameter that measures the effect of time, since it can directly reflect the influence of covariates on the effect of time-to-failure. The practical relevance of the proposed model is illustrated in a real problem based on a data set for downhole safety valves (DHSVs) used in offshore oil and gas production wells. The reliability estimation of DHSVs can be used, among others, to predict the blowout occurrence, assess the workover demand and aid decision-making actions.