A semi-empirical model for underground gas storage injection-production string time series remaining useful life analysis in process safety operation

Abstract

Time series risk analysis in digitalized process operations offers a useful means to transform the unordered data information into actions and practices related to process safety. A method for easily implementing time series degradation state analysis is proposed and described in terms of the remaining useful life (RUL) counting process based on a digitization semi-empirical probabilistic model. The well logs are used to inform a case study describing underground gas storage (UGS) injection-production string degradation risk. The corrosive effect of carbon dioxide in a complex environment is also considered, which leads to the decrease of internal pressure strength (IPS) with wall thickness thinning as well as RUL. The relationship between the residual IPS and RUL of the injection-production string at different rates of wall-thinning is analyzed. Based on the Gamma process method, the key empirical parameter information with time series degradation process and the probability density functions of different degradation under corresponding conditions are obtained. Then based on the Sequential Monte Carlo algorithm, the degradation trajectory of time series with confidence belt is obtained, and the confidence interval quantization of the wall thinning rate of injection-production string under different working conditions is realized. The results show that a semi-empirical time series RUL analysis could be used to more realistically evaluate the degradation state of the injection-production string. The residual strength with the Klever-Stewart calculation model has high stability for the uncertainty characteristics of the stochastic process. The proposed model is expected to calculate and determine the RUL of string with different thinning rates of wall thickness, and then further determine the time series corrosion degradation state for risk early warning. It is thus concluded that the proposed digitization semi-empirical remaining useful life prediction model provides a highly intuitive and quantitative tool for assessing the risk of degradation at the RUL level in the injection-production string of UGS.