Environmental consequence analysis of oil spills from onshore pipelines with parametric uncertainty

Abstract

Due to spatial heterogeneities, measurement errors and inherent errors of parameter estimation, hydrogeology parameters are not exactly known. Therefore, the accuracy of environmental consequence prediction for oil spills from onshore pipeline accidents is subject to the uncertainty of soil parameters. However, due to the time-consuming calculation of uncertainty analysis and the difficulty of modeling the cross-correlation among soil parameters, the correlativity is often overlooked in the current researches involving the impacts of uncertain soil parameters on the environmental consequence estimation. To address above concerns, this study introduces a novel methodological framework for investigating the contribution of correlated soil parameters on the environmental consequence caused by spilled oil from onshore oil pipeline accidents. The proposed methodological framework combines a polynomial chaos expansion(PCE) surrogate model and methods from Uncertainty Analysis (UA) and PAWN-based Global Sensitivity Analysis (GSA). In addition, the practical application of this framework is illustrated with a case study for estimating the contaminated range of spilled oil from onshore pipeline accidents.First of all, the correlation of soil parameters is modeled by the copula function, and a PCE surrogate model has been constructed in order to alleviate the computational burden of the following UA and PAWN-based GSA. Then, UA is performed to quantify the impacts of correlativity by means of Monte Carlo (MC) method. Results reveal the correlativity of soil parameters introduces a substantial variation in the estimation of contaminated depth of spilled oil. In addition, the UA results show that selecting different copula functions to describe the correlation of soil parameters may lead to different variations of contaminated depth. Moreover, the uncertainty of the calculation results has been reduced with the consideration of correlated soil parameters, which may make the calculation results more reliable. Finally, the PAWN-based GSA is performed to identify the most influential parameter on the variation of contaminated depth. Results of the GSA show that saturated permeability and scale parameter α are primarily responsible for the variability of the model output, while the porosity exerts a smaller influence. This finding also suggests the possibility of replacing the less influential parameters with its average value, thus appreciably reducing the computation cost of the problem. The results of GSA not only offer a better understanding of correlativity to the contaminated range of spilled oil, but also identify the parameters for which additional effort needs to be invested to reduce their uncertainty and, as a result, the uncertainty associated with environmental consequence estimation of onshore pipeline accidents.